Instructions to use maxkordn/Qwen3-32B-Solver-Biomni-paper11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maxkordn/Qwen3-32B-Solver-Biomni-paper11 with Transformers:

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

pipe = pipeline("text-generation", model="maxkordn/Qwen3-32B-Solver-Biomni-paper11")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maxkordn/Qwen3-32B-Solver-Biomni-paper11")
model = AutoModelForCausalLM.from_pretrained("maxkordn/Qwen3-32B-Solver-Biomni-paper11", 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 maxkordn/Qwen3-32B-Solver-Biomni-paper11 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maxkordn/Qwen3-32B-Solver-Biomni-paper11"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maxkordn/Qwen3-32B-Solver-Biomni-paper11",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maxkordn/Qwen3-32B-Solver-Biomni-paper11

SGLang

How to use maxkordn/Qwen3-32B-Solver-Biomni-paper11 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 "maxkordn/Qwen3-32B-Solver-Biomni-paper11" \
    --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": "maxkordn/Qwen3-32B-Solver-Biomni-paper11",
		"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 "maxkordn/Qwen3-32B-Solver-Biomni-paper11" \
        --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": "maxkordn/Qwen3-32B-Solver-Biomni-paper11",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maxkordn/Qwen3-32B-Solver-Biomni-paper11 with Docker Model Runner:
```
docker model run hf.co/maxkordn/Qwen3-32B-Solver-Biomni-paper11
```

maxkordn commited on Mar 11

Commit

7f81a6d

verified ·

1 Parent(s): ea42b07

End of training

Browse files

Files changed (2) hide show

README.md +4 -1
config.json +96 -0

README.md CHANGED Viewed

@@ -1,17 +1,20 @@
 ---
 base_model: Qwen/Qwen3-32B
 library_name: transformers
 model_name: Qwen3-32B-Solver-Biomni-paper11
 tags:
 - generated_from_trainer
 - sft
 - trl
 licence: license
 ---
 # Model Card for Qwen3-32B-Solver-Biomni-paper11
-This model is a fine-tuned version of [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start

 ---
 base_model: Qwen/Qwen3-32B
+datasets: flattened_successful_trajectories_paper11/v0
 library_name: transformers
 model_name: Qwen3-32B-Solver-Biomni-paper11
 tags:
 - generated_from_trainer
 - sft
+- cogzero
+- kordn
 - trl
 licence: license
 ---
 # Model Card for Qwen3-32B-Solver-Biomni-paper11
+This model is a fine-tuned version of [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) on the [flattened_successful_trajectories_paper11/v0](https://huggingface.co/datasets/flattened_successful_trajectories_paper11/v0) dataset.
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start

config.json ADDED Viewed

	@@ -0,0 +1,96 @@

+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 25600,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 64,
+  "model_type": "qwen3",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 64,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}