Instructions to use Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G",
	filename="Qwen3.5-35B-A3B-RAMP-v2-15g.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G
# Run inference directly in the terminal:
llama-cli -hf Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G
# Run inference directly in the terminal:
llama-cli -hf Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G
# Run inference directly in the terminal:
./llama-cli -hf Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G

Use Docker

docker model run hf.co/Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G

LM Studio
Jan

vLLM

How to use Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G

Ollama
How to use Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G with Ollama:
```
ollama run hf.co/Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G
```

Unsloth Studio new

How to use Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G to start chatting

Pi new

How to use Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G

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 Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G

Run Hermes

hermes

Docker Model Runner
How to use Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G with Docker Model Runner:
```
docker model run hf.co/Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G
```

Lemonade

How to use Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G

Run and chat with the model

lemonade run user.Qwen3.5-35B-A3B-RAMP-v2-15G-{{QUANT_TAG}}

List all available models

lemonade list

Qwen3.5-35B-A3B — RAMP v2 (15.2 GB)

Hardware-optimized GGUF quantization of Qwen3.5-35B-A3B for RTX 5060 Ti 16GB.

Produced with RAMP (RL-guided Adaptive Mixed-Precision), a data-free quantization pipeline that uses per-tensor sensitivity analysis and evolutionary search to find the optimal mixed-precision configuration for your specific hardware.

Key specs

Metric	Value
Base model	Qwen/Qwen3.5-35B-A3B
File size	15.2 GB
Average BPW	3.78
Base quant type	IQ3_S
Critical path overrides	Q8_0 (SSM gates, norms), Q6_K/Q5_K (attention QKV, shared expert)
Generation speed	~~80 tok/s on the chimere-server HTTP path (~~90 tok/s bare `ik_llama-server`), RTX 5060 Ti, sm_120, `-ngl 99 --n-cpu-moe 4`
Context	32K tokens (q8_0 keys + q4_0 values KV cache)
Functional benchmark	30/30
VRAM usage	~14 GB (GPU) + CPU experts

What makes RAMP different

Standard quantization applies the same precision to all tensors. RAMP assigns per-tensor precision based on sensitivity analysis:

SSM gates and norms → Q8_0 (critical for GDN recurrent state stability)
Attention Q/K/V projections → Q5_K/Q6_K (quality-sensitive)
MoE shared expert → Q5_K (always active, high impact)
MoE routed experts → IQ3_S (256 experts, only 8 active per token)

This is built with a custom imatrix calibrated on French + English + code + clinical (kiné) data, not generic wiki text.

How to use

# With ik_llama.cpp (recommended for sm_120 GPUs)
./llama-server \
  -m Qwen3.5-35B-A3B-RAMP-v2-15g.gguf \
  -ngl 99 --n-cpu-moe 4 \
  -np 1 -c 32768 \
  --cache-type-k q8_0 --cache-type-v q4_0

# With stock llama.cpp
./llama-server \
  -m Qwen3.5-35B-A3B-RAMP-v2-15g.gguf \
  -ngl 99 \
  -c 32768

Quantization pipeline

Start from Qwen3.5-35B-A3B BF16 (Unsloth GGUF)
Custom imatrix: domain-calibrated (BFCL + MoT + Codeforces + French clinical)
RAMP sensitivity analysis: per-tensor NSDS scoring (data-free)
llama-quantize --imatrix chimere --custom-q with 317 tensor overrides
Validation: 30/30 functional bench, perplexity check

Previous versions:

RAMP v1 (17 GB, Q3_K_M base, no imatrix) — backup
IQ3_S custom-mix (14.71 GB, 3.56 BPW) — backup

Hardware tested

GPU: NVIDIA RTX 5060 Ti 16GB (Blackwell, sm_120)
CPU: Intel i5-14600KF
RAM: 32GB DDR5
Driver: 590.48 (CUDA 12.8 toolkit)

chimere — Rust inference runtime
ramp-quant — Quantization pipeline source code
chimere-odo — Inference orchestrator

Author

Kevin Remondiere — Independent ML researcher, Bayonne, France

License

Apache 2.0 (quantization pipeline and model card). The base model follows Qwen's license.

Downloads last month: 15

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Quantized

(258)

this model

Kevletesteur
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Qwen3.5-35B-A3B-RAMP-v2-15G