Instructions to use a-m-team/AM-Thinking-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use a-m-team/AM-Thinking-v1 with Transformers:

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

pipe = pipeline("text-generation", model="a-m-team/AM-Thinking-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("a-m-team/AM-Thinking-v1")
model = AutoModelForCausalLM.from_pretrained("a-m-team/AM-Thinking-v1")
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
Local Apps Settings

vLLM

How to use a-m-team/AM-Thinking-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/a-m-team/AM-Thinking-v1

SGLang

How to use a-m-team/AM-Thinking-v1 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 "a-m-team/AM-Thinking-v1" \
    --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": "a-m-team/AM-Thinking-v1",
		"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 "a-m-team/AM-Thinking-v1" \
        --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": "a-m-team/AM-Thinking-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use a-m-team/AM-Thinking-v1 with Docker Model Runner:
```
docker model run hf.co/a-m-team/AM-Thinking-v1
```

reward for Non-Verifiable Queries

#12

by DaleMeng - opened Jun 24, 2025

Discussion

DaleMeng

Jun 24, 2025

Thanks for your great work!
I noticed that in your paper <DeepDistill: Enhancing LLM Reasoning Capabilities
via Large-Scale Difficulty-Graded Data Training>, mentioned that for Multi-turn Conversations and Others rewards, your choose the Decision-Tree-Reward-Llama-3.1-8B model to evaluate three dimensions and get the final average score, so i assumed you also use the same reward model for Non-Verifiable Queries here, I want to know if is there any special reason to choose the Reward-Llama-3.1-8B model?
According to their https://rlhflow.github.io/posts/2025-01-22-decision-tree-reward-model/ technical report, it indeed achieves in the SOTA in RewardBench v1, but I believe the decision tree part plays an important part to achieve the the SOTA results for how to use the different dimension of score. But in your paper, just use the coherence + correctness + helpfulness value directly and calculate the average, maybe other weighted average method can perform better？

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