Instructions to use Fate-Zero/Archer-Code-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fate-Zero/Archer-Code-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="Fate-Zero/Archer-Code-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Fate-Zero/Archer-Code-1.5B")
model = AutoModelForCausalLM.from_pretrained("Fate-Zero/Archer-Code-1.5B", 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 Fate-Zero/Archer-Code-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fate-Zero/Archer-Code-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fate-Zero/Archer-Code-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fate-Zero/Archer-Code-1.5B

SGLang

How to use Fate-Zero/Archer-Code-1.5B 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 "Fate-Zero/Archer-Code-1.5B" \
    --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": "Fate-Zero/Archer-Code-1.5B",
		"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 "Fate-Zero/Archer-Code-1.5B" \
        --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": "Fate-Zero/Archer-Code-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Fate-Zero/Archer-Code-1.5B with Docker Model Runner:
```
docker model run hf.co/Fate-Zero/Archer-Code-1.5B
```

Improve model card: Add metadata tags and sample usage

by nielsr HF Staff - opened Jul 22, 2025

base: refs/heads/main

←

from: refs/pr/1

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README.md +48 -0

README.md CHANGED Viewed

@@ -1,5 +1,12 @@
 ---
 license: apache-2.0
 ---
 <div align="center">
@@ -44,6 +51,47 @@ The Archer series focuses on research into RL algorithms and training for medium
 **Current Models**:
 - **[Archer-Code-1.5B](https://huggingface.co/Fate-Zero/Archer-Code-1.5B)** - SOTA among similarly-sized models.
 ## Evaluation
 We conduct evaluation on both mathematical and coding benchmarks. Due to the high variance of the outputs from reasoning models, we report avg@K (pass@1 performance averaged over K outputs) and pass@K for each benchmark. The detailed results are shown in the table below.

 ---
 license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - reasoning
+  - code-generation
+  - math
+  - qwen2
 ---
 <div align="center">
 **Current Models**:
 - **[Archer-Code-1.5B](https://huggingface.co/Fate-Zero/Archer-Code-1.5B)** - SOTA among similarly-sized models.
+## Sample Usage
+You can use the model with the `transformers` library:
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+# Load model and tokenizer
+model_id = "Fate-Zero/Archer-Code-1.5B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16, # Use torch.float16 if bfloat16 is not supported on your GPU
+    device_map="auto"
+)
+# Prepare input for code generation
+prompt = "Write a Python function to calculate the nth Fibonacci number."
+messages = [
+    {"role": "user", "content": prompt},
+]
+input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+model_inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+# Generate response
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    eos_token_id=tokenizer.eos_token_id
+)
+# Decode and print
+response_text = tokenizer.decode(generated_ids[0][model_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(response_text)
+```
 ## Evaluation
 We conduct evaluation on both mathematical and coding benchmarks. Due to the high variance of the outputs from reasoning models, we report avg@K (pass@1 performance averaged over K outputs) and pass@K for each benchmark. The detailed results are shown in the table below.