Instructions to use NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct")
model = AutoModelForCausalLM.from_pretrained("NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct

SGLang

How to use NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct with Docker Model Runner:
```
docker model run hf.co/NAM00/context_tuned_patient_matching_Llama-3.2-1B-Instruct
```

NAM00 commited on Mar 30, 2025

Commit

0c84aa0

verified ·

1 Parent(s): cc2594e

Trained patient-matching model with context-tuning

Browse files

Files changed (2) hide show

README.md +8 -7
model.safetensors +1 -1

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.6590
 ## Model description
@@ -35,7 +35,7 @@ More information needed
 ### Training hyperparameters
 The following hyperparameters were used during training:
-- learning_rate: 3e-05
 - train_batch_size: 1
 - eval_batch_size: 1
 - seed: 42
@@ -43,17 +43,18 @@ The following hyperparameters were used during training:
 - total_train_batch_size: 8
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: cosine
 - num_epochs: 5
 ### Training results
 | Training Loss | Epoch  | Step | Validation Loss |
 |:-------------:|:------:|:----:|:---------------:|
-| 13.9396       | 1.0    | 933  | 1.6778          |
-| 13.2393       | 2.0    | 1866 | 1.6715          |
-| 13.1078       | 3.0    | 2799 | 1.6800          |
-| 13.0497       | 4.0    | 3732 | 1.6581          |
-| 13.0853       | 4.9949 | 4660 | 1.6590          |
 ### Framework versions

 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 1.6620
 ## Model description
 ### Training hyperparameters
 The following hyperparameters were used during training:
+- learning_rate: 6e-05
 - train_batch_size: 1
 - eval_batch_size: 1
 - seed: 42
 - total_train_batch_size: 8
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 1000
 - num_epochs: 5
 ### Training results
 | Training Loss | Epoch  | Step | Validation Loss |
 |:-------------:|:------:|:----:|:---------------:|
+| 21.2622       | 1.0    | 933  | 1.7363          |
+| 13.5357       | 2.0    | 1866 | 1.6952          |
+| 13.1942       | 3.0    | 2799 | 1.6826          |
+| 13.0838       | 4.0    | 3732 | 1.6599          |
+| 13.0959       | 4.9949 | 4660 | 1.6620          |
 ### Framework versions

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4d0e53cc8dd5b8ab7690592c15d39e838669a505b62164f9d96401eb493be9fb
 size 2471645608

 version https://git-lfs.github.com/spec/v1
+oid sha256:96cb3517f98807c5b70929ed89a2f2436f1114316b05129d461b8ceb3284e595
 size 2471645608