Instructions to use cam-1000/MNLP_M3_rag_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cam-1000/MNLP_M3_rag_model with Transformers:

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

pipe = pipeline("text-generation", model="cam-1000/MNLP_M3_rag_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cam-1000/MNLP_M3_rag_model")
model = AutoModelForCausalLM.from_pretrained("cam-1000/MNLP_M3_rag_model")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use cam-1000/MNLP_M3_rag_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cam-1000/MNLP_M3_rag_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cam-1000/MNLP_M3_rag_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cam-1000/MNLP_M3_rag_model

SGLang

How to use cam-1000/MNLP_M3_rag_model 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 "cam-1000/MNLP_M3_rag_model" \
    --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": "cam-1000/MNLP_M3_rag_model",
		"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 "cam-1000/MNLP_M3_rag_model" \
        --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": "cam-1000/MNLP_M3_rag_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cam-1000/MNLP_M3_rag_model with Docker Model Runner:
```
docker model run hf.co/cam-1000/MNLP_M3_rag_model
```

cam-1000 commited on Jun 10, 2025

Commit

303b73e

verified ·

1 Parent(s): fa6b2e1

Update config.json

Browse files

Files changed (1) hide show

config.json +9 -2

config.json CHANGED Viewed

@@ -17,14 +17,21 @@
   "num_attention_heads": 16,
   "num_hidden_layers": 28,
   "num_key_value_heads": 8,
   "rms_norm_eps": 1e-06,
   "rope_scaling": null,
   "rope_theta": 1000000,
   "sliding_window": null,
   "tie_word_embeddings": true,
   "torch_dtype": "bfloat16",
-  "transformers_version": "4.52.3",
-  "use_cache": false,
   "use_sliding_window": false,
   "vocab_size": 151936
 }

   "num_attention_heads": 16,
   "num_hidden_layers": 28,
   "num_key_value_heads": 8,
+  "rag_config": {
+    "embedding_model_name":    "sentence-transformers/all-mpnet-base-v2",
+    "retrieval_top_k":         5,
+    "index_type":              "faiss",
+    "similarity_function":     "cosine",
+    "max_input_length":        512
+  },
   "rms_norm_eps": 1e-06,
   "rope_scaling": null,
   "rope_theta": 1000000,
   "sliding_window": null,
   "tie_word_embeddings": true,
   "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
   "use_sliding_window": false,
   "vocab_size": 151936
 }