Instructions to use TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1 with Transformers:

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

pipe = pipeline("text-generation", model="TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1")
model = AutoModelForCausalLM.from_pretrained("TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1

SGLang

How to use TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1 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 "TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1" \
    --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": "TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1",
		"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 "TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1" \
        --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": "TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1 with Docker Model Runner:
```
docker model run hf.co/TongZheng1999/FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1
```

TongZheng1999 commited on Apr 13, 2025

Commit

77de91e

verified ·

1 Parent(s): f8870d4

End of training

Browse files

Files changed (2) hide show

README.md +1 -0
config.json +1 -1

README.md CHANGED Viewed

@@ -4,6 +4,7 @@ library_name: transformers
 model_name: FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1
 tags:
 - generated_from_trainer
 - trl
 - sft
 licence: license

 model_name: FL_1000_MoT_Llama-3.1-8B-It-star-mixed_direct-OP-final_v2_1-2-3Rounds-iter-1
 tags:
 - generated_from_trainer
+- alignment-handbook
 - trl
 - sft
 licence: license

config.json CHANGED Viewed

@@ -33,6 +33,6 @@
   "torch_dtype": "bfloat16",
   "transformers_version": "4.46.0",
   "unsloth_fixed": true,
-  "use_cache": false,
   "vocab_size": 128256
 }

   "torch_dtype": "bfloat16",
   "transformers_version": "4.46.0",
   "unsloth_fixed": true,
+  "use_cache": true,
   "vocab_size": 128256
 }