Instructions to use talxcc/Tals-coder-flash-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use talxcc/Tals-coder-flash-01 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf talxcc/Tals-coder-flash-01
# Run inference directly in the terminal:
llama cli -hf talxcc/Tals-coder-flash-01

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf talxcc/Tals-coder-flash-01
# Run inference directly in the terminal:
llama cli -hf talxcc/Tals-coder-flash-01

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 talxcc/Tals-coder-flash-01
# Run inference directly in the terminal:
./llama-cli -hf talxcc/Tals-coder-flash-01

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 talxcc/Tals-coder-flash-01
# Run inference directly in the terminal:
./build/bin/llama-cli -hf talxcc/Tals-coder-flash-01

Use Docker

docker model run hf.co/talxcc/Tals-coder-flash-01

LM Studio
Jan

vLLM

How to use talxcc/Tals-coder-flash-01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "talxcc/Tals-coder-flash-01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "talxcc/Tals-coder-flash-01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/talxcc/Tals-coder-flash-01

Ollama
How to use talxcc/Tals-coder-flash-01 with Ollama:
```
ollama run hf.co/talxcc/Tals-coder-flash-01
```
Unsloth Desktop

How to use talxcc/Tals-coder-flash-01 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf talxcc/Tals-coder-flash-01

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "talxcc/Tals-coder-flash-01"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use talxcc/Tals-coder-flash-01 with Docker Model Runner:
```
docker model run hf.co/talxcc/Tals-coder-flash-01
```

Lemonade

How to use talxcc/Tals-coder-flash-01 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull talxcc/Tals-coder-flash-01

Run and chat with the model

lemonade run user.Tals-coder-flash-01-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use talxcc/Tals-coder-flash-01 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf talxcc/Tals-coder-flash-01

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 talxcc/Tals-coder-flash-01

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use talxcc/Tals-coder-flash-01 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf talxcc/Tals-coder-flash-01

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 "talxcc/Tals-coder-flash-01" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

Tals-coder-flash-01 / README.md

talxcc

Add professional liability disclaimer and responsible use guidelines

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	---
	language:
	- en
	- multilingual
	tags:
	- dynamic-quant
	- asymmetric-quant
	- speculative-decoding
	- mtp
	- gguf
	- custom-quant
	- qwen35
	- code
	- reasoning
	- 27b
	base_model: Qwen/Qwen3.8-27B
	license: apache-2.0
	pipeline_tag: text-generation
	---

	<div align="center">

	# ⚡ Tals-coder-flash-01 (27B Asymmetric Dynamic Quant + Native MTP)

	### 99.9% Coding Performance of Q4_K_M at 3.73 BPW with High-Speed MTP Speculative Decoding

	<p align="center">
	<img src="flash.gif" width="600" alt="The Flash Lightning Speed" />
	</p>

	### "Why run bloated 15GB Q4 models that crawl at 19 tok/s? Tals-coder-flash-01 delivers 99.9% coding fidelity at 34–40 tok/s on consumer GPUs."

	</div>

	---

	> [!NOTE]
	> Hardware Testing Notice: All speed benchmarks and token generation speeds reported below were tested on an NVIDIA GeForce RTX 4060 Ti 16GB at stock factory settings without any overclocking.

	---

	## 🚀 The Asymmetric Dynamic Quant Breakthrough

	Tals-coder-flash-01 is a custom Asymmetric Dynamic Quantization of [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B). By dynamically distributing precision across network dimensions based on mathematical importance, it hits a sweet spot of `3.73 BPW` (Bits Per Weight) and an ultra-lean 11.86 GB footprint (compared to 15.2 GB for standard Q4_K_M).

	### 🎯 Key Highlights:
	* 🏆 99.9% Coding Retention: Matches unsloth Q4_K_M within 0.1% on coding tasks (HumanEval, MBPP, multi-file code generation, and complex refactoring).
	* 📊 Near-Lossless General Benches: General reasoning benchmarks (MMLU, GSM8k) land within just 0.3 – 0.5 points of full Q4_K_M, while saving 3.3+ GB of VRAM.
	* ⚡ Significantly Faster Than Base Q4_K_M: Because the dense model is only 11.86 GB, it slashes memory bus transit time per token by over 22%.
	* 💨 Blazing MTP Speculative Speeds:
	* Up to 34 tokens/sec sustained generation.
	* Up to 40 tokens/sec peak bursts on boilerplate and repetitive syntax.
	* Comparison: Base Q3_K_L with MTP is capped at 29–30 tok/s; standard Q4_K_M without MTP crawls at only 19–21 tok/s.

	---

	## 📊 Comprehensive Comparison Table

	(All speed benchmarks measured on a stock, non-overclocked NVIDIA GeForce RTX 4060 Ti 16GB)

	\| Model Variant \| Effective BPW \| Model Size \| Coding Retention \| General Benches (vs Q4_K_M) \| Speed (No MTP) \| Speed (With MTP) \|
	\| :--- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \|
	\| Unsloth Q4_K_M (Standard) \| 4.50 BPW \| 15.20 GB \| 100% (Baseline) \| Baseline (100%) \| 19 – 21 tok/s \| ~26 – 28 tok/s \|
	\| Standard Q3_K_L \| 3.52 BPW \| 11.40 GB \| ~94.2% \| -1.8 to -2.4 pts \| 21 – 23 tok/s \| 29 – 30 tok/s \|
	\| ⚡ Tals-coder-flash-01 \| `3.73 BPW` \| 11.86 GB \| 99.9% \| -0.3 to -0.5 pts \| 24 – 26 tok/s \| 34 – 40 tok/s \|

	---

	## 💻 Hardware Compatibility & VRAM Sizing Guide

	Because of its lean 11.86 GB footprint, Tals-coder-flash-01 unlocks extreme context windows across consumer GPUs without ever touching slow system RAM:

	### 1. 12 GB VRAM GPUs (RTX 3060 12GB, RTX 4070 12GB)
	* Model Footprint: 11.86 GB fits completely in VRAM.
	* Context Capacity: 8,192 tokens (8k context) fully offloaded to GPU with zero CPU spillover!

	### 2. 16 GB VRAM GPUs (RTX 4060 Ti 16GB, RTX 4070 Ti Super 16GB, RTX 4080 16GB)
	* High-Precision Mode: Up to 64,000 context (64k) with FP16 KV Cache (`-ctk f16 -ctv f16`) for zero precision degradation.
	* Balanced Mode: 128,000 context (128k) with Hybrid `K=q8_0`, `V=q4_0` cache (~14.1 GB total VRAM).
	* Extreme Long-Context: Up to 200,000+ context (200k) with `K=q4_0`, `V=q4_0` cache fitting 100% on GPU!

	---

	## 🛠️ Optimal llama.cpp Runtime Setup

	Run with the optimized `llama.cpp` server for maximum speculative decoding throughput:

	### Recommended Environment Variables:
	```bat
	set GGML_CUDA_ROWLANE=1
	set GGML_CUDA_RL_N4_LONG=1
	set CUDA_DEVICE_SCHEDULE=BLOCKING_SYNC
	```

	---

	## ⚡ Recommended Run Commands

	### 1. 16GB GPUs — Default 128k High-Speed Coding Mode (34–40 tok/s)
	```bash
	./llama-server \
	-m "Tals-coder-flash-01.gguf" \
	-ngl 99 \
	-c 128000 \
	-ctk q8_0 -ctv q4_0 \
	-fa on -np 1 -t 4 -tb 4 --poll 0 \
	--load-mode mmap --reasoning-preserve \
	--spec-type draft-mtp --spec-draft-n-max 2 --spec-draft-p-min 0.2 \
	--backend-sampling --jinja \
	--host 127.0.0.1 --port 8080
	```

	### 2. 16GB GPUs — 64k Full FP16 KV Precision Mode
	```bash
	./llama-server \
	-m "Tals-coder-flash-01.gguf" \
	-ngl 99 \
	-c 65536 \
	-ctk f16 -ctv f16 \
	-fa on -np 1 -t 4 -tb 4 --poll 0 \
	--spec-type draft-mtp --spec-draft-n-max 2 --spec-draft-p-min 0.2 \
	--backend-sampling --host 127.0.0.1 --port 8080
	```

	### 3. 12GB GPUs — 8k Standard VRAM Mode
	```bash
	./llama-server \
	-m "Tals-coder-flash-01.gguf" \
	-ngl 99 \
	-c 8192 \
	-ctk q8_0 -ctv q8_0 \
	-fa on -np 1 -t 4 -tb 4 --poll 0 \
	--spec-type draft-mtp --spec-draft-n-max 2 --spec-draft-p-min 0.2 \
	--backend-sampling --host 127.0.0.1 --port 8080
	```

	---

	## 💻 OpenCode / Cline / Continue Configuration

	Connect your favorite coding agent with deterministic settings:
	* Base URL: `http://127.0.0.1:8080/v1`
	* Model Name: `tals-coder-flash-01`
	* API Key: `not-needed` (any string)
	* Temperature: `0.0` – `0.2` (Deterministic coding & reasoning)

	---

	## 🙏 Credits & Acknowledgements

	* [Qwen Team](https://huggingface.co/Qwen): For the exceptional foundation of the [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) architecture.

	---

	## ⚖️ Disclaimer & Responsible Use

	This model is provided "as is" for research, development, and educational purposes. The creators, authors, and contributors assume no liability or responsibility for any actions, automated executions, code implementations, direct or consequential damages, or loss resulting from the deployment, generation, or misuse of this model or its outputs.

	Downstream developers and users are solely responsible for verifying, reviewing, sandboxing, and testing any generated code or reasoning outputs prior to execution in production environments or system-critical applications, as well as maintaining compliance with local regulations and ethical AI practices. Please use responsibly.

	Distributed under the Apache 2.0 license.