Instructions to use jpacifico/Aramis-2B-BitNet-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jpacifico/Aramis-2B-BitNet-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="jpacifico/Aramis-2B-BitNet-bf16", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jpacifico/Aramis-2B-BitNet-bf16", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jpacifico/Aramis-2B-BitNet-bf16", trust_remote_code=True, 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jpacifico/Aramis-2B-BitNet-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jpacifico/Aramis-2B-BitNet-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jpacifico/Aramis-2B-BitNet-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jpacifico/Aramis-2B-BitNet-bf16

SGLang

How to use jpacifico/Aramis-2B-BitNet-bf16 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 "jpacifico/Aramis-2B-BitNet-bf16" \
    --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": "jpacifico/Aramis-2B-BitNet-bf16",
		"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 "jpacifico/Aramis-2B-BitNet-bf16" \
        --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": "jpacifico/Aramis-2B-BitNet-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jpacifico/Aramis-2B-BitNet-bf16 with Docker Model Runner:
```
docker model run hf.co/jpacifico/Aramis-2B-BitNet-bf16
```

jpacifico commited on Aug 15, 2025

Commit

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@@ -12,19 +12,19 @@ tags:
 ---
 # model Summary
-**bitnet-dpo-merged-modelstock7** (≈2B, BitNet b1.58)
 A compact, agent-oriented small language model focused on language understanding and contextual decision-making.
 Built with an iterative post-training recipe: bilingual DPO (FR+EN) + model merging of FR-centric and EN-centric variants.
 Runs natively as BitNet 1.58-bit (ternary) and is available in GGUF 1.58-bit, lossless to the BF16 checkpoints.
-**Why BitNet (and why this model)**
 	•	BitNet b1.58 uses ternary weights (−1,0,+1) with abs-mean scaling : very low memory & energy, great CPU/edge throughput, unlike classic FP/INT SLMs.
 	•	ModelStock7 demonstrates that a 2B BitNet can deliver SOTA language understanding in its class without sacrificing efficiency.
 # Training Recipe
--Bilingual DPO (FR+EN) to sharpen preference selection across two languages.
--Model merging (FR-centric + EN-centric) to broaden stylistic/lexical coverage.
 Goal: agent-oriented behavior → better instruction following, contextual disambiguation, and pragmatic reasoning in multi-turn settings.
 # First benchmarks

 ---
 # model Summary
+**bitnet-dpo-merged-modelstock7** (≈2B, BitNet b1.58)
 A compact, agent-oriented small language model focused on language understanding and contextual decision-making.
 Built with an iterative post-training recipe: bilingual DPO (FR+EN) + model merging of FR-centric and EN-centric variants.
 Runs natively as BitNet 1.58-bit (ternary) and is available in GGUF 1.58-bit, lossless to the BF16 checkpoints.
+**Why BitNet (and why this model)**
 	•	BitNet b1.58 uses ternary weights (−1,0,+1) with abs-mean scaling : very low memory & energy, great CPU/edge throughput, unlike classic FP/INT SLMs.
 	•	ModelStock7 demonstrates that a 2B BitNet can deliver SOTA language understanding in its class without sacrificing efficiency.
 # Training Recipe
+-Bilingual DPO (FR+EN) to sharpen preference selection across two languages.
+-Model merging (FR-centric + EN-centric) to broaden stylistic/lexical coverage.
 Goal: agent-oriented behavior → better instruction following, contextual disambiguation, and pragmatic reasoning in multi-turn settings.
 # First benchmarks