diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml
index a512997..10a17b3 100644
--- a/.github/workflows/main.yaml
+++ b/.github/workflows/main.yaml
@@ -28,7 +28,7 @@ jobs:
           python-version: ${{ matrix.python-version }}
       - name: Install dependencies
         run: |
-          uv sync --extra dev --extra docs --extra vllm
+          uv sync --extra dev --extra docs --extra llm
           uv run python -m ensurepip
       - name: Check types
         run: |
diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml
index 0b862b5..c3cd5b7 100644
--- a/.github/workflows/release.yaml
+++ b/.github/workflows/release.yaml
@@ -41,7 +41,7 @@ jobs:
             python-version: "3.10"
       - name: Install dependencies
         run: |
-          uv sync --extra dev --extra docs --extra vllm
+          uv sync --extra dev --extra docs --extra llm
       - name: Run unit tests
         run: |
           uv run pytest -v tests/app --cov --cov-report=html:coverage_reports #--random-order
diff --git a/README.md b/README.md
index 599660b..20e0279 100644
--- a/README.md
+++ b/README.md
@@ -230,6 +230,30 @@ curl -N -X 'POST' 'http://127.0.0.1:8000/stream/generate?max_tokens=512' \
     -d 'What is hypertension?'
 ```
 
+CMS also provides APIs that are compatible with the OpenAI client. For example, using its Python SDK, you can generate text and retrieve embeddings with the following snippet:
+```python
+from openai import OpenAI
+
+client = OpenAI(api_key="dummy", base_url="http://localhost:8000/v1")
+completion = client.chat.completions.create(
+    model="Huggingface LLM Model",
+    messages=[
+        {"role": "system", "content": "You are bot answering questions from the user"},
+        {"role": "user", "content": "What is hypertension?"},
+    ],
+    max_tokens=256,
+    temperature=0.7,
+    stream=False
+)
+print(f"CMS => {completion.choices[0].message.content}")
+embeddings = client.embeddings.create(
+    model="Huggingface LLM Model", input="What is hypertension?"
+)
+```
+Note that to enable quantization and training features, you need to install the extra dependencies:
+```commandline
+pip install '.[llm]'
+```
 #### Chat with served models
 You can also "chat" with the running model using the `/stream/ws` endpoint. For example:
 ```html
diff --git a/app/api/routers/generative.py b/app/api/routers/generative.py
index 4d88459..761fb51 100644
--- a/app/api/routers/generative.py
+++ b/app/api/routers/generative.py
@@ -4,7 +4,7 @@
 import uuid
 import app.api.globals as cms_globals
 
-from typing import Union, Iterable, AsyncGenerator
+from typing import Union, Iterable, AsyncGenerator, List
 from typing_extensions import Annotated
 from functools import partial
 from fastapi import APIRouter, Depends, Request, Body, Query
@@ -51,7 +51,9 @@ def generate_text(
     request: Request,
     prompt: Annotated[str, Body(description="The prompt to be sent to the model", media_type="text/plain")],
     max_tokens: Annotated[int, Query(description="The maximum number of tokens to generate", gt=0)] = 512,
-    temperature: Annotated[float, Query(description="The temperature of the generated text", gt=0.0, lt=1.0)] = 0.7,
+    temperature: Annotated[float, Query(description="The temperature of the generated text", ge=0.0)] = 0.7,
+    top_p: Annotated[float, Query(description="The Top-P value for nucleus sampling", ge=0.0, le=1.0)] = 0.9,
+    stop_sequences: Annotated[List[str], Query(description="The list of sequences used to stop the generation")] = [],
     tracking_id: Union[str, None] = Depends(validate_tracking_id),
     model_service: AbstractModelService = Depends(cms_globals.model_service_dep)
 ) -> PlainTextResponse:
@@ -63,6 +65,8 @@ def generate_text(
         prompt (str): The prompt to be sent to the model.
         max_tokens (int): The maximum number of tokens to generate.
         temperature (float): The temperature of the generated text.
+        top_p (float): The Top-P value for nucleus sampling.
+        stop_sequences (List[str]): The list of sequences used to stop the generation.
         tracking_id (Union[str, None]): An optional tracking ID of the requested task.
         model_service (AbstractModelService): The model service dependency.
 
@@ -77,6 +81,8 @@ def generate_text(
                 prompt,
                 max_tokens=max_tokens,
                 temperature=temperature,
+                top_p=top_p,
+                stop_sequences=stop_sequences,
                 report_tokens=partial(_send_usage_metrics, handler=PATH_GENERATE),
             ),
             headers={"x-cms-tracking-id": tracking_id},
@@ -101,7 +107,9 @@ async def generate_text_stream(
     request: Request,
     prompt: Annotated[str, Body(description="The prompt to be sent to the model", media_type="text/plain")],
     max_tokens: Annotated[int, Query(description="The maximum number of tokens to generate", gt=0)] = 512,
-    temperature: Annotated[float, Query(description="The temperature of the generated text", gt=0.0, lt=1.0)] = 0.7,
+    temperature: Annotated[float, Query(description="The temperature of the generated text", ge=0.0)] = 0.7,
+    top_p: Annotated[float, Query(description="The Top-P value for nucleus sampling", ge=0.0, le=1.0)] = 0.9,
+    stop_sequences: Annotated[List[str], Query(description="The list of sequences used to stop the generation")] = [],
     tracking_id: Union[str, None] = Depends(validate_tracking_id),
     model_service: AbstractModelService = Depends(cms_globals.model_service_dep)
 ) -> StreamingResponse:
@@ -113,6 +121,8 @@ async def generate_text_stream(
         prompt (str): The prompt to be sent to the model.
         max_tokens (int): The maximum number of tokens to generate.
         temperature (float): The temperature of the generated text.
+        top_p (float): The Top-P value for nucleus sampling.
+        stop_sequences (List[str]): The list of sequences used to stop the generation.
         tracking_id (Union[str, None]): An optional tracking ID of the requested task.
         model_service (AbstractModelService): The model service dependency.
 
@@ -127,6 +137,8 @@ async def generate_text_stream(
                 prompt,
                 max_tokens=max_tokens,
                 temperature=temperature,
+                top_p=top_p,
+                stop_sequences=stop_sequences,
                 report_tokens=partial(_send_usage_metrics, handler=PATH_GENERATE_ASYNC),
             ),
             media_type="text/event-stream",
@@ -162,7 +174,7 @@ def generate_chat_completions(
 
     Args:
         request (Request): The request object.
-        request_data (OpenAIChatRequest): The request data containing model, messages, and stream.
+        request_data (OpenAIChatRequest): The request data containing model, messages, stream, temperature, top_p, and stop_sequences.
         tracking_id (Union[str, None]): An optional tracking ID of the requested task.
         model_service (AbstractModelService): The model service dependency.
 
@@ -176,6 +188,8 @@ def generate_chat_completions(
     stream = request_data.stream
     max_tokens = request_data.max_tokens
     temperature = request_data.temperature
+    top_p = request_data.top_p
+    stop_sequences = request_data.stop_sequences
     tracking_id = tracking_id or str(uuid.uuid4())
 
     if not messages:
@@ -193,7 +207,7 @@ def generate_chat_completions(
             headers={"x-cms-tracking-id": tracking_id},
         )
 
-    async def _stream(prompt: str, max_tokens: int, temperature: float) -> AsyncGenerator:
+    async def _stream(prompt: str, max_tokens: int, temperature: float, top_p: float, stop_sequences: List[str]) -> AsyncGenerator:
         data = {
             "id": tracking_id,
             "object": "chat.completion.chunk",
@@ -204,6 +218,8 @@ async def _stream(prompt: str, max_tokens: int, temperature: float) -> AsyncGene
             prompt,
             max_tokens=max_tokens,
             temperature=temperature,
+            top_p=top_p,
+            stop_sequences=stop_sequences,
             report_tokens=partial(_send_usage_metrics, handler=PATH_OPENAI_COMPLETIONS)
         ):
             data = {
@@ -221,7 +237,7 @@ async def _stream(prompt: str, max_tokens: int, temperature: float) -> AsyncGene
     prompt = get_prompt_from_messages(model_service.tokenizer, messages)
     if stream:
         return StreamingResponse(
-            _stream(prompt, max_tokens, temperature),
+            _stream(prompt, max_tokens, temperature, top_p, stop_sequences or []),
             media_type="text/event-stream",
             headers={"x-cms-tracking-id": tracking_id},
         )
@@ -230,6 +246,8 @@ async def _stream(prompt: str, max_tokens: int, temperature: float) -> AsyncGene
             prompt,
             max_tokens=max_tokens,
             temperature=temperature,
+            top_p=top_p,
+            stop_sequences=stop_sequences or [],
             send_metrics=partial(_send_usage_metrics, handler=PATH_OPENAI_COMPLETIONS),
         )
         completion = OpenAIChatResponse(
diff --git a/app/api/utils.py b/app/api/utils.py
index 6cf38cd..e869164 100644
--- a/app/api/utils.py
+++ b/app/api/utils.py
@@ -323,8 +323,8 @@ async def init_vllm_engine(app: FastAPI,
         )
         from vllm import SamplingParams, TokensPrompt
     except ImportError:
-        logger.error("Cannot import the vLLM engine. Please install it with `pip install cms[vllm]`.")
-        raise ExtraDependencyRequiredException("Cannot import the vLLM engine. Please install it with `pip install cms[vllm]`.")
+        logger.error("Cannot import the vLLM engine. Please install it with `pip install cms[llm]`.")
+        raise ExtraDependencyRequiredException("Cannot import the vLLM engine. Please install it with `pip install cms[llm]`.")
 
     parser = FlexibleArgumentParser()
     parser = make_arg_parser(parser)
diff --git a/app/cli/cli.py b/app/cli/cli.py
index eefa4db..1fb9a48 100644
--- a/app/cli/cli.py
+++ b/app/cli/cli.py
@@ -490,7 +490,7 @@ def package_model(
         raise typer.Exit(code=1)
 
     if output_model_package == "":
-        typer.echo("ERROR: The model package path is not passed in.")
+        typer.echo("ERROR: The output model package path is not passed in.")
         raise typer.Exit(code=1)
 
     model_package_archive = os.path.abspath(os.path.expanduser(output_model_package))
diff --git a/app/domain.py b/app/domain.py
index bceaa21..655eafe 100644
--- a/app/domain.py
+++ b/app/domain.py
@@ -217,6 +217,8 @@ class OpenAIChatRequest(BaseModel):
     max_tokens: int = Field(512, description="The maximum number of tokens to generate", gt=0)
     model: str = Field(..., description="The name of the model used for generating the completion")
     temperature: float = Field(0.7, description="The temperature of the generated text", ge=0.0, le=1.0)
+    top_p: float = Field(0.9, description="The top-p value for nucleus sampling", ge=0.0, le=1.0)
+    stop_sequences: Optional[List[str]] = Field(default=None, description="The list of sequences used to stop the generation")
 
 
 class OpenAIChatResponse(BaseModel):
diff --git a/app/management/model_manager.py b/app/management/model_manager.py
index 25d95ab..90e8b72 100644
--- a/app/management/model_manager.py
+++ b/app/management/model_manager.py
@@ -293,7 +293,7 @@ def predict_stream(
             for _, item in df.iterrows():
                 yield item.to_dict()
 
-    @staticmethod   # type: ignore
-    @func_deprecated
+    @staticmethod
+    @func_deprecated()
     def _get_pip_requirements() -> str:
         return os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "requirements.txt"))
diff --git a/app/model_services/huggingface_llm_model.py b/app/model_services/huggingface_llm_model.py
index df128c7..bc28bfd 100644
--- a/app/model_services/huggingface_llm_model.py
+++ b/app/model_services/huggingface_llm_model.py
@@ -230,8 +230,12 @@ def batch_annotate(self, texts: List[str]) -> List[List[Annotation]]:
     def generate(
         self,
         prompt: str,
+        min_tokens: int = 100,
         max_tokens: int = 512,
+        num_beams: int = 5,
         temperature: float = 0.7,
+        top_p: float = 0.9,
+        stop_sequences: Optional[List[str]] = None,
         report_tokens: Optional[Callable[[str], None]] = None,
         **kwargs: Any
     ) -> str:
@@ -240,8 +244,12 @@ def generate(
 
         Args:
             prompt (str): The prompt for the text generation
+            min_tokens (int): The minimum number of tokens to generate. Defaults to 100.
             max_tokens (int): The maximum number of tokens to generate. Defaults to 512.
+            num_beams (int): The number of beams for beam search. Defaults to 5.
             temperature (float): The temperature for the text generation. Defaults to 0.7.
+            top_p (float): The Top-P value for nucleus sampling. Defaults to 0.9.
+            stop_sequences (Optional[List[str]]): List of strings that will stop generation when encountered. Defaults to None.
             report_tokens (Optional[Callable[[str], None]]): The callback function to send metrics. Defaults to None.
             **kwargs (Any): Additional keyword arguments to be passed to this method.
 
@@ -257,14 +265,25 @@ def generate(
         generation_kwargs = dict(
             inputs=inputs.input_ids,
             attention_mask=inputs.attention_mask,
+            min_new_tokens=min_tokens,
             max_new_tokens=max_tokens,
-            do_sample=False,
+            num_beams=num_beams,
+            do_sample=True,
             temperature=temperature,
-            top_p=0.9,
+            top_p=top_p,
+            repetition_penalty=1.2,
+            no_repeat_ngram_size=3,
         )
 
         outputs = self.model.generate(**generation_kwargs)
         generated_text = self.tokenizer.decode(outputs[0], skip_prompt=True, skip_special_tokens=True)
+
+        if stop_sequences:
+            for stop_seq in stop_sequences:
+                if stop_seq in generated_text:
+                    generated_text = generated_text.split(stop_seq)[0]
+                    break
+
         logger.debug("Response generation completed")
 
         if report_tokens:
@@ -280,6 +299,8 @@ async def generate_async(
         prompt: str,
         max_tokens: int = 512,
         temperature: float = 0.7,
+        top_p: float = 0.9,
+        stop_sequences: Optional[List[str]] = None,
         report_tokens: Optional[Callable[[str], None]] = None,
         **kwargs: Any
     ) -> AsyncIterable:
@@ -290,6 +311,8 @@ async def generate_async(
             prompt (str): The prompt for the text generation.
             max_tokens (int): The maximum number of tokens to generate. Defaults to 512.
             temperature (float): The temperature for the text generation. Defaults to 0.7.
+            top_p (float): The Top-P value for nucleus sampling. Defaults to 0.9.
+            stop_sequences (Optional[List[str]]): List of strings that will stop generation when encountered. Defaults to None.
             report_tokens (Optional[Callable[[str], None]]): The callback function to send metrics. Defaults to None.
             **kwargs (Any): Additional keyword arguments to be passed to the model loader.
 
@@ -314,15 +337,25 @@ async def generate_async(
             max_new_tokens=max_tokens,
             do_sample=True,
             temperature=temperature,
-            top_p=0.9,
+            top_p=top_p,
+            repetition_penalty=1.2,
+            no_repeat_ngram_size=3,
         )
 
         try:
             _ = self._text_generator.submit(self.model.generate, **generation_kwargs)
             output = ""
             for content in streamer:
-                yield content
+                prev_output = output
                 output += content
+                if stop_sequences:
+                    for stop_seq in stop_sequences:
+                        if stop_seq in output:
+                            remaining = output[len(prev_output):output.find(stop_seq)]
+                            if remaining:
+                                yield remaining
+                            return
+                yield content
                 await asyncio.sleep(0.01)
             if report_tokens:
                 report_tokens(
diff --git a/app/trainers/huggingface_llm_trainer.py b/app/trainers/huggingface_llm_trainer.py
index 6084c46..5350422 100644
--- a/app/trainers/huggingface_llm_trainer.py
+++ b/app/trainers/huggingface_llm_trainer.py
@@ -15,7 +15,6 @@
     TrainingArguments,
     PreTrainedModel,
     PreTrainedTokenizerBase,
-    PreTrainedTokenizerFast,
     TrainerCallback,
     TrainerState,
     TrainerControl,
@@ -34,13 +33,11 @@
     get_model_data_package_base_name,
 )
 from app.trainers.base import SupervisedTrainer
-from app.domain import ModelType, TrainerBackend, LlmRole, LlmTrainerType, LlmDatasetType, PromptMessage, Device
+from app.domain import ModelType, TrainerBackend, LlmRole, LlmTrainerType, LlmDatasetType, PromptMessage
 from app.exception import (
     TrainingCancelledException,
-    ManagedModelException,
     DatasetException,
     ConfigurationException,
-    DeviceNotAvailableError,
     ExtraDependencyRequiredException,
 )
 if TYPE_CHECKING:
@@ -81,9 +78,6 @@ class HuggingFaceLlmSupervisedTrainer(SupervisedTrainer, _HuggingFaceLlmTrainerC
     CONTINUING_TOKEN_LABEL_ID = 1
 
     def __init__(self, model_service: "HuggingFaceLlmModel") -> None:
-        if not isinstance(model_service.tokenizer, PreTrainedTokenizerFast):
-            logger.error("The supervised trainer requires a fast tokenizer to function correctly")
-            raise ManagedModelException("The supervised trainer requires a fast tokenizer to function correctly")
         SupervisedTrainer.__init__(self, model_service._config, model_service.model_name)
         self._model_service = model_service
         self._model_name = model_service.model_name
@@ -299,15 +293,12 @@ def run(
             run_id (str): The run ID of the training job.
             description (Optional[str]): The optional description of the training or change logs.
         """
-
-        if self._config.DEVICE is not Device.GPU.value:
-            raise DeviceNotAvailableError("This trainer currently requires a CUDA device")
-
         try:
             from trl import GRPOConfig, GRPOTrainer  # , PPOConfig, PPOTrainer
-        except ImportError:
-            logger.error("Cannot import the GRPO Trainer. Please install it with `pip install cms[vllm]`.")
-            raise ExtraDependencyRequiredException("Cannot import the GRPO Trainer. Please install it with `pip install cms[vllm]`.")
+        except ImportError as e:
+            logger.exception(e)
+            logger.error("Cannot import the GRPO Trainer. Please install it with `pip install cms[llm]`.")
+            raise ExtraDependencyRequiredException("Cannot import the GRPO Trainer. Please install it with `pip install cms[llm]`.")
 
         trained_model_pack_path = None
         redeploy = self._config.REDEPLOY_TRAINED_MODEL == "true"
diff --git a/docker-compose-dev.yml b/docker-compose-dev.yml
index fb41b5b..8a3e183 100644
--- a/docker-compose-dev.yml
+++ b/docker-compose-dev.yml
@@ -236,6 +236,47 @@ services:
       - https_proxy=$HTTPS_PROXY
       - no_proxy=localhost
 
+  huggingface-llm:
+    extends:
+      file: ./docker-compose.yml
+      service: huggingface-llm
+    labels:
+      - org.cogstack.model-serve.dev=true
+    build:
+      context: ./
+      dockerfile: ./docker/huggingface-llm/Dockerfile
+      args:
+        - CMS_MODEL_NAME=HuggingFace LLM model
+        - CMS_UID=${CMS_UID:-1000}
+        - CMS_GID=${CMS_GID:-1000}
+        - HTTP_PROXY=$HTTP_PROXY
+        - HTTPS_PROXY=$HTTPS_PROXY
+        - NO_PROXY=$NO_PROXY
+    image: local-cms-huggingface-llm:do-not-push
+    restart: always
+    networks:
+      - cms
+    environment:
+      - BASE_MODEL_FULL_PATH=$MODEL_PACKAGE_FULL_PATH
+      - AWS_ACCESS_KEY_ID=
+      - AWS_SECRET_ACCESS_KEY=
+      - MLFLOW_S3_ENDPOINT_URL=
+      - MLFLOW_TRACKING_URI=${MLFLOW_TRACKING_URI:-file:/tmp/mlruns}
+      - MLFLOW_TRACKING_USERNAME=
+      - MLFLOW_TRACKING_PASSWORD=
+      - MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING=${MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING:-false}
+      - GELF_INPUT_URI=
+      - AUTH_USER_ENABLED=${AUTH_USER_ENABLED:-false}
+      - AUTH_JWT_SECRET=$AUTH_JWT_SECRET
+      - AUTH_ACCESS_TOKEN_EXPIRE_SECONDS=${AUTH_ACCESS_TOKEN_EXPIRE_SECONDS:-3600}
+      - AUTH_DATABASE_URL=${AUTH_DATABASE_URL:-sqlite+aiosqlite:///./cms-users.db}
+      - HTTP_PROXY=$HTTP_PROXY
+      - HTTPS_PROXY=$HTTPS_PROXY
+      - NO_PROXY=localhost
+      - http_proxy=$HTTP_PROXY
+      - https_proxy=$HTTPS_PROXY
+      - no_proxy=localhost
+
 volumes:
   retrained-models:
     driver: local
diff --git a/docker-compose.yml b/docker-compose.yml
index c486b3c..12f2b64 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -286,6 +286,53 @@ services:
       retries: 3
       start_period: 60s
 
+  huggingface-llm:
+    image: cogstacksystems/cogstack-modelserve:0.1.0
+    labels:
+      - org.cogstack.model-serve=huggingface_llm
+      - org.cogstack.model-name=HuggingFace LLM model
+      - org.cogstack.model-path=$MODEL_PACKAGE_FULL_PATH
+    restart: always
+    networks:
+      - cms
+    volumes:
+      - ${MODEL_PACKAGE_FULL_PATH}:/app/model/model.zip:ro
+      - retrained-models:/app/model/retrained:rw
+      - ./docker/huggingface-llm/.env:/app/envs/.env:ro
+    environment:
+      - BASE_MODEL_FULL_PATH=$MODEL_PACKAGE_FULL_PATH
+      - CMS_MODEL_TYPE=huggingface_ner
+      - CMS_MODEL_NAME=HuggingFace NER model
+      - CMS_STREAMABLE=${CMS_STREAMABLE:-false}
+      - AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID
+      - AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY
+      - MLFLOW_S3_ENDPOINT_URL=${MLFLOW_S3_ENDPOINT_URL:-http://minio:9000}
+      - MLFLOW_TRACKING_URI=${MLFLOW_TRACKING_URI:-http://mlflow-ui:5000}
+      - MLFLOW_TRACKING_USERNAME=${MLFLOW_TRACKING_USERNAME:-admin}
+      - MLFLOW_TRACKING_PASSWORD=${MLFLOW_TRACKING_PASSWORD:-password}
+      - MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING=${MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING:-true}
+      - GELF_INPUT_URI=${GELF_INPUT_URI:-http://graylog:12201}
+      - AUTH_USER_ENABLED=${AUTH_USER_ENABLED:-false}
+      - AUTH_JWT_SECRET=$AUTH_JWT_SECRET
+      - AUTH_ACCESS_TOKEN_EXPIRE_SECONDS=${AUTH_ACCESS_TOKEN_EXPIRE_SECONDS:-3600}
+      - AUTH_DATABASE_URL=${AUTH_DATABASE_URL:-sqlite+aiosqlite:///./cms-users.db}
+      - HTTP_PROXY=$HTTP_PROXY
+      - HTTPS_PROXY=$HTTPS_PROXY
+      - NO_PROXY=mlflow-ui,minio,graylog,auth-db,localhost
+      - http_proxy=$HTTP_PROXY
+      - https_proxy=$HTTPS_PROXY
+      - no_proxy=mlflow-ui,minio,graylog,auth-db,localhost
+    expose:
+      - 8000
+    ports:
+      - 8186:8000
+    healthcheck: # readiness check
+      test: [ "CMD", "curl", "-f", "http://localhost:8000/info" ]
+      interval: 1m30s
+      timeout: 10s
+      retries: 3
+      start_period: 60s
+
 volumes:
   retrained-models:
     driver: local
diff --git a/docker/huggingface-llm/.dockerignore b/docker/huggingface-llm/.dockerignore
new file mode 100644
index 0000000..90e2e80
--- /dev/null
+++ b/docker/huggingface-llm/.dockerignore
@@ -0,0 +1,2 @@
+app/model/*
+app/mlruns/*
\ No newline at end of file
diff --git a/docker/huggingface-llm/Dockerfile b/docker/huggingface-llm/Dockerfile
new file mode 100644
index 0000000..800bea1
--- /dev/null
+++ b/docker/huggingface-llm/Dockerfile
@@ -0,0 +1,43 @@
+FROM python:3.10
+LABEL "org.cogstack.model-serve"="huggingface_llm"
+
+ARG CMS_MODEL_NAME
+ARG HTTP_PROXY
+ARG HTTPS_PROXY
+ARG NO_PROXY
+ARG CMS_UID=1000
+ARG CMS_GID=1000
+
+ENV CMS_MODEL_NAME=$CMS_MODEL_NAME
+ENV CMS_MODEL_TYPE=huggingface_ner
+ENV HTTP_PROXY=$HTTP_PROXY
+ENV HTTPS_PROXY=$HTTPS_PROXY
+ENV NO_PROXY=$NO_PROXY
+ENV http_proxy=$HTTP_PROXY
+ENV https_proxy=$HTTPS_PROXY
+ENV no_proxy=$NO_PROXY
+ENV PYTHONUNBUFFERED=1
+ENV PATH="/home/cms/.local/bin:${PATH}"
+
+RUN apt-get update && apt-get install -y --no-install-recommends nano telnet && \
+    rm -rf /var/lib/apt/lists/*
+RUN addgroup --gid $CMS_GID cms || true && \
+    adduser --uid $CMS_UID --gid $CMS_GID --disabled-password --gecos "" cms || true && \
+    echo "cms ALL=(ALL) NOPASSWD: ALL" >> /etc/sudoers
+
+COPY app /app
+COPY docker/huggingface-ner/requirements.txt /app/requirements.txt
+COPY docker/entrypoint/serve.sh /app/entrypoint.sh
+RUN mkdir -p /app/model/model && \
+    mkdir -p /app/model/retrained && \
+    chown -R $CMS_UID:$CMS_GID /app
+RUN python -m venv .venv && \
+    /.venv/bin/pip install --no-cache-dir -U pip &&\
+    /.venv/bin/pip install --no-cache-dir -r /app/requirements.txt &&\
+    chown -R $CMS_UID:$CMS_GID /.venv && \
+    chmod +x /app/entrypoint.sh
+
+WORKDIR /app
+EXPOSE 8000
+USER cms:cms
+CMD ["/app/entrypoint.sh"]
diff --git a/docker/huggingface-llm/requirements.txt b/docker/huggingface-llm/requirements.txt
new file mode 100644
index 0000000..00fc031
--- /dev/null
+++ b/docker/huggingface-llm/requirements.txt
@@ -0,0 +1,31 @@
+medcat[spacy,meta-cat,deid,rel-cat]~=2.2.0
+datasets>=2.21.0
+fastapi~=0.115.0
+uvicorn~=0.29.0
+python-multipart~=0.0.7
+ijson~=3.1.4
+python-dotenv~=0.20.0
+mlflow~=2.16.2
+psycopg2-binary~=2.9.4
+boto3~=1.28.84
+click<8.2.0
+typer~=0.15.1
+prometheus-fastapi-instrumentator~=7.0.0
+sentencepiece~=0.2.0
+slowapi~=0.1.7
+graypy~=2.1.0
+fastapi-users~=13.0.0
+fastapi-users-db-sqlalchemy~=5.0.0
+asyncpg~=0.27.0
+aiosqlite~=0.19.0
+evaluate~=0.4.1
+websockets~=12.0
+pynvml~=11.5.3
+toml~=0.10.2
+peft<0.14.0
+huggingface-hub~=0.33.0
+vllm~=0.8.5
+trl>=0.11.4
+bitsandbytes>=0.49.0
+setuptools
+wheel
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 8b9be40..ffb26c7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,7 +64,7 @@ dev = [
     "pytest-cov~=4.1.0",
     "pytest-bdd~=7.2.0",
     "httpx~=0.24.1",
-    "mypy~=1.14.0",
+    "mypy~=1.18.0",
     "ruff==0.6.9",
     "locust<2.32.0",
     "typer-cli~=0.15.1",
@@ -78,11 +78,10 @@ docs = [
     "sphinx-autodoc-typehints~=2.0.1",
     "sphinx-rtd-theme~=3.0.2",
 ]
-
-vllm = [
+llm = [
     "vllm~=0.8.5",
-    "trl>=0.11.4",
-    "bitsandbytes>=0.45.5",
+    "trl~=0.15.0",
+    "bitsandbytes==0.49.0",
 ]
 
 # For pip versions not supporting PEP 735
@@ -96,7 +95,7 @@ dev = [
     "pytest-cov~=4.1.0",
     "pytest-bdd~=7.2.0",
     "httpx~=0.24.1",
-    "mypy~=1.14.0",
+    "mypy~=1.18.0",
     "ruff==0.6.9",
     "locust<2.32.0",
     "typer-cli~=0.15.1",
@@ -111,7 +110,7 @@ docs = [
     "sphinx-rtd-theme~=3.0.2",
 ]
 
-vllm = [
+llm = [
     "vllm~=0.8.5",
     "trl>=0.11.4",
     "bitsandbytes>=0.45.5",
diff --git a/tests/app/api/test_serving_hf_llm.py b/tests/app/api/test_serving_hf_llm.py
index 9b7ea9f..c01fd89 100644
--- a/tests/app/api/test_serving_hf_llm.py
+++ b/tests/app/api/test_serving_hf_llm.py
@@ -43,7 +43,7 @@ def client(llm_model_service):
 
 def test_generate(client):
     response = client.post(
-        "/generate?max_tokens=128&temperature=0.7",
+        "/generate?max_tokens=128&temperature=0.7&top_p=0.9&stop_sequences=end",
         data="Alright?",
         headers={"Content-Type": "text/plain"},
     )
@@ -59,7 +59,7 @@ async def test_stream_generate(llm_model_service, llm_app):
     llm_model_service.generate_async.return_value = "Fine."
     async with httpx.AsyncClient(app=llm_app, base_url="http://test") as ac:
         response = await ac.post(
-            "/stream/generate?max_tokens=32&temperature=0.7",
+            "/stream/generate?max_tokens=32&temperature=0.7&top_p=0.9&stop_sequences=end",
             data="How are you doing?",
             headers={"Content-Type": "text/plain"},
         )
diff --git a/tests/app/model_services/test_huggingface_llm_model.py b/tests/app/model_services/test_huggingface_llm_model.py
index e6ee637..6fbb89d 100644
--- a/tests/app/model_services/test_huggingface_llm_model.py
+++ b/tests/app/model_services/test_huggingface_llm_model.py
@@ -57,8 +57,12 @@ def test_generate(huggingface_llm_model):
 
     result = huggingface_llm_model.generate(
         prompt="Alright?",
+        min_tokens=50,
         max_tokens=128,
+        num_beams=2,
         temperature=0.5,
+        top_p=0.8,
+        stop_sequences=["end"],
         report_tokens=mock_send_metrics
     )
 
@@ -70,10 +74,14 @@ def test_generate(huggingface_llm_model):
     huggingface_llm_model.model.generate.assert_called_once_with(
         inputs=inputs.input_ids,
         attention_mask=inputs.attention_mask,
+        min_new_tokens=50,
         max_new_tokens=128,
-        do_sample=False,
+        num_beams=2,
+        do_sample=True,
         temperature=0.5,
-        top_p=0.9,
+        top_p=0.8,
+        repetition_penalty=1.2,
+        no_repeat_ngram_size=3,
     )
     huggingface_llm_model.tokenizer.decode.assert_called_once_with(
         outputs[0],
@@ -102,8 +110,12 @@ async def test_generate_async(huggingface_llm_model):
 
     result = await huggingface_llm_model.generate_async(
         prompt="Alright?",
+        min_tokens=50,
         max_tokens=128,
+        num_beams=2,
         temperature=0.5,
+        top_p=0.8,
+        stop_sequences=["end"],
         report_tokens=mock_send_metrics
     )
 
@@ -115,10 +127,14 @@ async def test_generate_async(huggingface_llm_model):
     huggingface_llm_model.model.generate_async.assert_called_once_with(
         inputs=inputs.input_ids,
         attention_mask=inputs.attention_mask,
+        min_new_tokens=50,
         max_new_tokens=128,
-        do_sample=False,
+        num_beams=2,
+        do_sample=True,
         temperature=0.5,
-        top_p=0.9,
+        top_p=0.8,
+        repetition_penalty=1.2,
+        no_repeat_ngram_size=3,
     )
     huggingface_llm_model.tokenizer.decode.assert_called_once_with(
         outputs[0],
diff --git a/tests/app/trainers/test_hf_llm_trainer.py b/tests/app/trainers/test_hf_llm_trainer.py
index 65e9d3b..67ea601 100644
--- a/tests/app/trainers/test_hf_llm_trainer.py
+++ b/tests/app/trainers/test_hf_llm_trainer.py
@@ -6,6 +6,14 @@
 from app.trainers.huggingface_llm_trainer import HuggingFaceLlmSupervisedTrainer
 from app.config import Settings
 
+
+def _triton_installed():
+    try:
+        import triton
+        return True
+    except ImportError:
+        return False
+
 model_parent_dir = os.path.join(os.path.dirname(__file__), "..", "..", "resources", "fixture")
 config = Settings()
 config.MLFLOW_TRACKING_URI = "http://localhost:5000"
@@ -40,11 +48,12 @@ def test_deploy_model():
     assert model_service.tokenizer == tokenizer
 
 
+@skipIf(not _triton_installed(), "This requires triton to be installed")
 def test_huggingface_llm_supervised_trainer(mlflow_fixture):
     with patch.object(supervised_trainer, "run", wraps=supervised_trainer.run) as run:
         supervised_trainer._tracker_client = Mock()
         supervised_trainer._tracker_client.start_tracking = Mock(return_value=("experiment_id", "run_id"))
-        with open(os.path.join(data_dir, "trainer_export.json"), "r") as f:
+        with open(os.path.join(data_dir, "sample_qa.json"), "r") as f:
             supervised_trainer.train(f, 1, 1, "training_id", "input_file_name")
     supervised_trainer._tracker_client.start_tracking.assert_called_once()
     run.assert_called_once()
@@ -52,5 +61,5 @@ def test_huggingface_llm_supervised_trainer(mlflow_fixture):
 
 @skipIf(config.DEVICE != "cuda", "This requires a CUDA device to run")
 def test_huggingface_llm_supervised_run(mlflow_fixture):
-    with open(os.path.join(data_dir, "trainer_export.json"), "r") as data_file:
+    with open(os.path.join(data_dir, "sample_qa.json"), "r") as data_file:
         HuggingFaceLlmSupervisedTrainer.run(supervised_trainer, {"nepochs": 1, "print_stats": 1}, data_file, 1, "run_id")
diff --git a/tests/resources/fixture/sample_qa.json b/tests/resources/fixture/sample_qa.json
new file mode 100644
index 0000000..c520a6b
--- /dev/null
+++ b/tests/resources/fixture/sample_qa.json
@@ -0,0 +1,22 @@
+[
+    {
+        "question": "What is high blood pressure?",
+        "answer": "High blood pressure is a condition where the force at which your heart pumps blood around your body is high. It is recorded with 2 numbers, the systolic pressure and the diastolic pressure, both measured in millimetres of mercury (mmHg)."
+    },
+    {
+        "question": "What are the risks of high blood pressure?",
+        "answer": "Persistent high blood pressure can put extra strain on your blood vessels, heart, and other organs, such as the brain, kidneys, and eyes. It can increase your risk of serious and potentially life-threatening health conditions, such as heart disease, heart attacks, strokes, heart failure, peripheral arterial disease, aortic aneurysms, kidney disease, and vascular dementia."
+    },
+    {
+        "question": "How is high blood pressure diagnosed?",
+        "answer": "The only way of knowing whether you have high blood pressure is to have a blood pressure test, which can be done at your GP surgery, some pharmacies, as part of your NHS Health Check, or in some workplaces. All adults over 40 are advised to have their blood pressure checked at least every 5 years."
+    },
+    {
+        "question": "What are the lifestyle changes that can help reduce blood pressure?",
+        "answer": "Lifestyle changes that can help prevent and lower high blood pressure include reducing the amount of salt you eat and having a generally healthy diet, cutting back on alcohol, losing weight if you're overweight, exercising regularly, cutting down on caffeine, and stopping smoking."
+    },
+    {
+        "question": "What are the medicines used to control high blood pressure?",
+        "answer": "Common blood pressure medicines include ACE inhibitors, angiotensin-2 receptor blockers (ARBs), calcium channel blockers, diuretics, beta blockers, alpha blockers, and other diuretics. The medicine recommended for you will depend on factors like how high your blood pressure is, your age, and your ethnicity."
+    }
+]
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
index 07a65b5..6414061 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,18 +2,10 @@ version = 1
 revision = 3
 requires-python = ">3.10, <3.13"
 resolution-markers = [
-    "python_full_version >= '3.12' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "python_full_version > '3.11' and python_full_version < '3.12' and sys_platform == 'darwin'",
-    "python_full_version > '3.11' and python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version > '3.11' and python_full_version < '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version > '3.11' and python_full_version < '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "python_full_version == '3.11' and sys_platform == 'darwin'",
-    "python_full_version == '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version == '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "python_full_version < '3.11' and sys_platform == 'darwin'",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "python_full_version >= '3.12' and sys_platform != 'win32'",
+    "python_full_version > '3.11' and python_full_version < '3.12' and sys_platform != 'win32'",
+    "python_full_version == '3.11' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and sys_platform != 'win32'",
     "python_full_version >= '3.12' and sys_platform == 'win32'",
     "python_full_version > '3.11' and python_full_version < '3.12' and sys_platform == 'win32'",
     "python_full_version == '3.11' and sys_platform == 'win32'",
@@ -22,7 +14,7 @@ resolution-markers = [
 
 [[package]]
 name = "accelerate"
-version = "1.7.0"
+version = "1.12.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
@@ -33,9 +25,9 @@ dependencies = [
     { name = "safetensors" },
     { name = "torch" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/97/33/47bbd507e3a851d33d19ce7b2141c5ea3689bfae91ba168044d7db24b0e9/accelerate-1.7.0.tar.gz", hash = "sha256:e8a2a5503d6237b9eee73cc8d36cf543f9c2d8dd2c6713450b322f5e6d53a610", size = 376026, upload-time = "2025-05-15T10:00:52.117Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/4a/8e/ac2a9566747a93f8be36ee08532eb0160558b07630a081a6056a9f89bf1d/accelerate-1.12.0.tar.gz", hash = "sha256:70988c352feb481887077d2ab845125024b2a137a5090d6d7a32b57d03a45df6", size = 398399, upload-time = "2025-11-21T11:27:46.973Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f8/bb/be8146c196ad6e4dec78385d91e92591f8a433576c4e04c342a636fcd811/accelerate-1.7.0-py3-none-any.whl", hash = "sha256:cf57165cca28769c6cf2650812371c81b18e05743dfa3c748524b1bb4f2b272f", size = 362095, upload-time = "2025-05-15T10:00:49.914Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/d2/c581486aa6c4fbd7394c23c47b83fa1a919d34194e16944241daf9e762dd/accelerate-1.12.0-py3-none-any.whl", hash = "sha256:3e2091cd341423207e2f084a6654b1efcd250dc326f2a37d6dde446e07cabb11", size = 380935, upload-time = "2025-11-21T11:27:44.522Z" },
 ]
 
 [[package]]
@@ -49,7 +41,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.11.18"
+version = "3.13.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -61,68 +53,72 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/63/e7/fa1a8c00e2c54b05dc8cb5d1439f627f7c267874e3f7bb047146116020f9/aiohttp-3.11.18.tar.gz", hash = "sha256:ae856e1138612b7e412db63b7708735cff4d38d0399f6a5435d3dac2669f558a", size = 7678653, upload-time = "2025-04-21T09:43:09.191Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/c7/c3/e5f64af7e97a02f547020e6ff861595766bb5ecb37c7492fac9fe3c14f6c/aiohttp-3.11.18-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:96264854fedbea933a9ca4b7e0c745728f01380691687b7365d18d9e977179c4", size = 711703, upload-time = "2025-04-21T09:40:25.487Z" },
-    { url = "https://files.pythonhosted.org/packages/5f/2f/53c26e96efa5fd01ebcfe1fefdfb7811f482bb21f4fa103d85eca4dcf888/aiohttp-3.11.18-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9602044ff047043430452bc3a2089743fa85da829e6fc9ee0025351d66c332b6", size = 471348, upload-time = "2025-04-21T09:40:27.569Z" },
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