Instructions to use wxzhang/selective-pairrm-33045197-mt0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wxzhang/selective-pairrm-33045197-mt0 with Transformers:

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

pipe = pipeline("text-generation", model="wxzhang/selective-pairrm-33045197-mt0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wxzhang/selective-pairrm-33045197-mt0")
model = AutoModelForCausalLM.from_pretrained("wxzhang/selective-pairrm-33045197-mt0", 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 wxzhang/selective-pairrm-33045197-mt0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wxzhang/selective-pairrm-33045197-mt0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wxzhang/selective-pairrm-33045197-mt0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wxzhang/selective-pairrm-33045197-mt0

SGLang

How to use wxzhang/selective-pairrm-33045197-mt0 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 "wxzhang/selective-pairrm-33045197-mt0" \
    --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": "wxzhang/selective-pairrm-33045197-mt0",
		"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 "wxzhang/selective-pairrm-33045197-mt0" \
        --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": "wxzhang/selective-pairrm-33045197-mt0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wxzhang/selective-pairrm-33045197-mt0 with Docker Model Runner:
```
docker model run hf.co/wxzhang/selective-pairrm-33045197-mt0
```

wxzhang commited on Mar 21, 2024

Commit

a5665b2

verified ·

1 Parent(s): 1ec4438

End of training

Browse files

Files changed (2) hide show

README.md +5 -1
config.json +1 -1

README.md CHANGED Viewed

@@ -2,9 +2,13 @@
 license: apache-2.0
 base_model: mistralai/Mistral-7B-Instruct-v0.2
 tags:
 - trl
 - dpo
 - generated_from_trainer
 model-index:
 - name: selective-pairrm-33045197-mt0
   results: []
@@ -15,7 +19,7 @@ should probably proofread and complete it, then remove this comment. -->
 # selective-pairrm-33045197-mt0
-This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.6825
 - Rewards/chosen: -0.2329

 license: apache-2.0
 base_model: mistralai/Mistral-7B-Instruct-v0.2
 tags:
+- alignment-handbook
+- generated_from_trainer
 - trl
 - dpo
 - generated_from_trainer
+datasets:
+- snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset
 model-index:
 - name: selective-pairrm-33045197-mt0
   results: []
 # selective-pairrm-33045197-mt0
+This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.6825
 - Rewards/chosen: -0.2329

config.json CHANGED Viewed

@@ -21,6 +21,6 @@
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.36.2",
-  "use_cache": false,
   "vocab_size": 32000
 }

   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.36.2",
+  "use_cache": true,
   "vocab_size": 32000
 }