Instructions to use continuum-ai/qwen2.5-coder-7b-compacted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use continuum-ai/qwen2.5-coder-7b-compacted with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("continuum-ai/qwen2.5-coder-7b-compacted")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use continuum-ai/qwen2.5-coder-7b-compacted with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "continuum-ai/qwen2.5-coder-7b-compacted"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "continuum-ai/qwen2.5-coder-7b-compacted"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use continuum-ai/qwen2.5-coder-7b-compacted with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "continuum-ai/qwen2.5-coder-7b-compacted"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "continuum-ai/qwen2.5-coder-7b-compacted"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "continuum-ai/qwen2.5-coder-7b-compacted",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use continuum-ai/qwen2.5-coder-7b-compacted with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "continuum-ai/qwen2.5-coder-7b-compacted"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default continuum-ai/qwen2.5-coder-7b-compacted

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use continuum-ai/qwen2.5-coder-7b-compacted with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "continuum-ai/qwen2.5-coder-7b-compacted"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "continuum-ai/qwen2.5-coder-7b-compacted" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

12% Pruned, 61.0 HUMANEVAL (base 62.2)

Qwen2.5-Coder-7B recovered to within calibration tolerance of the unmodified base via KL-distillation compensation LoRA.

HUMANEVAL: 61.0 (base 62.2, Δ -1.2)
HUMANEVAL+PLUS: 53.0 (base 53.7, Δ -0.7)

Every claim on this card is verified
Trust: self-attested · 2 benchmarks · 1 device tested
ForgeAlloy chain of custody · Download alloy · Merkle-chained

Qwen2.5-Coder-7B with cryptographic provenance via the ForgeAlloy chain of custody. Scores 61.0 humaneval against the unmodified base's 62.2, recovered to within calibration tolerance after head pruning + distillation. Ships with the per-problem evaluation outputs so the score is independently verifiable.

Benchmarks

Benchmark	Score	Base	Δ	Verified
humaneval	61.0	62.2	-1.2	✅ Result hash
humaneval_plus	53.0	53.7	-0.7	✅ Result hash

What Changed (Base → Forged)

	Base	Forged	Delta
Pruning	None	12% heads (activation-magnitude)	-12% params ✅
compensation-lora	None	rank=16	q_proj, k_proj, v_proj, o_proj...
Pipeline		prune → lora → lora → eval	1 cycles

Runs On

Device	Format	Size	Speed
NVIDIA GeForce RTX 5090	fp16	—	Verified
MacBook Pro 32GB	fp16	8.0GB	Expected
MacBook Air 16GB	Q8_0	~4.0GB	Expected
MacBook Air 8GB	Q4_K_M	~2.5GB	Expected
iPhone / Android	Q4_K_M	~2.5GB	Expected

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("continuum-ai/v2-7b-coder-compensated",
    torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("continuum-ai/v2-7b-coder-compensated")

inputs = tokenizer("def merge_sort(arr):", return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Methodology

Produced via head pruning, LoRA fine-tuning, KL-distillation compensation against the unmodified teacher. Full methodology, ablations, and per-stage rationale are in the methodology paper and the companion MODEL_METHODOLOGY.md in this repository. The pipeline ran as prune → lora → lora → eval over 1 cycle on NVIDIA GeForce RTX 5090.

Limitations

This model is currently a methodology demonstration rather than a Pareto-optimal artifact at any specific hardware tier. For production code workloads on smaller hardware, the unmodified Qwen2.5-Coder-7B at standard quantization (Q4_K_M / Q5_K_M / Q8_0) may be a better fit pending the larger Qwen3.5+ forges that exercise the pruning dimension where this methodology actually wins.
Validated on HumanEval / HumanEval+ for English-language Python code completion. Performance on other programming languages, code paradigms (functional, embedded, kernel), or code-adjacent domains (SQL, regex, shell) has not been measured.
Ships as fp16 only. GGUF quantization tiers (Q5_K_S / Q3_K_M / Q2_K) are not yet published for this artifact; the per-tier comparison from the development log showed base+quant dominates v2+quant at every VRAM tier on the same 7B base, which is why the methodology validation here uses fp16 and the production GGUF publishes are reserved for the Qwen3.5+ forges where the dimension flips.
Vision modality not yet wired in. The Continuum sensory architecture treats vision as first-class for personas, but this 7B coder artifact is text-only.

Chain of Custody

Scan the QR or verify online. Download the alloy file to verify independently.

What	Proof
Model weights	`sha256:156247b9f9b25d302651e2540f1dad58d...`
Forged on	NVIDIA GeForce RTX 5090, ?
Published	huggingface — 2026-04-08T05:02:57.072577+00:00
Trust level	`self-attested`
Spec	ForgeAlloy — Rust/Python/TypeScript

Make Your Own

Forged with Continuum — a distributed AI world that runs on your hardware.

The Factory configurator lets you design and forge custom models visually — context extension, pruning, LoRA, quantization, vision/audio modalities. Pick your target devices, the system figures out what fits.

GitHub · All Models · Forge-Alloy