[whatmodel.sh](../../README.md) / xDAN-L1-Chat-RL-v1
# xDAN-L1-Chat-RL-v1
xDAN-AI · released 2023-12 · 7.24B params · cc-by-4.0
**score 37.4** ±0.8 · #603 of 732 · from 3 benchmark results across 1 source
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks xDAN-L1-Chat-RL-v1 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #417 of 469 100% 3
coding · #417 of 469 · 100% of its score
Beats [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [DeepSeek R1 Distill Llama 8B](deepseek-r1-distill-llama-8b.md) here, though they rank higher overall. Trails [Llama 3.2 1B Instruct](llama-3-2-1b.md), [SmolLM2 1.7B Instruct](smollm2-1-7b.md), [DeepSeek LLM 7B Chat](deepseek-llm-7b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 40.2% ▁·▁▁▃●▆▅█▃▁▁ beats 23/99 easy · avg 76% 53% sharp · at its level
HumanEval+ 32.9% ▁·▁▁▁▃●▄▄█▃▁ beats 22/99 easy · avg 70% 39% sharp · at its level
MBPP+ 41.3% ▁···▁▁●▅▇█▆▂ beats 7/75 easy · avg 66% 8.3% at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 100% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 0% ▇
weighted average = 37.4
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run xDAN-L1-Chat-RL-v1 (Q4_K_M, needs ~4.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/xDAN-L1-Chat-RL-v1-GGUF/tree/0d75d8fe750b13cc897696fa3ef518e2e8b60481))
```sh
llama cli -hf TheBloke/xDAN-L1-Chat-RL-v1-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/xDAN-L1-Chat-RL-v1-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `llama cli -hf TheBloke/xDAN-L1-Chat-RL-v1-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run xDAN-L1-Chat-RL-v1 (Q4_K_M, needs ~4.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/xDAN-L1-Chat-RL-v1-GGUF/tree/0d75d8fe750b13cc897696fa3ef518e2e8b60481))
```sh
lms get https://huggingface.co/TheBloke/xDAN-L1-Chat-RL-v1-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~7.7 GB) `lms get https://huggingface.co/TheBloke/xDAN-L1-Chat-RL-v1-GGUF@Q8_0 -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run xDAN-L1-Chat-RL-v1 (Q4_K_M, needs ~4.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/xDAN-L1-Chat-RL-v1-GGUF/tree/0d75d8fe750b13cc897696fa3ef518e2e8b60481))
```sh
ollama run hf.co/TheBloke/xDAN-L1-Chat-RL-v1-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `ollama run hf.co/TheBloke/xDAN-L1-Chat-RL-v1-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (fp32, needs ~28.97 GB, from [huggingface.co/xDAN-AI](https://huggingface.co/xDAN-AI/xDAN-L1-Chat-RL-v1/tree/232b7883284e0bf2ef776afe9701dfa583a69cdb))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='xDAN-AI/xDAN-L1-Chat-RL-v1', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/xDAN-AI](https://huggingface.co/xDAN-AI/xDAN-L1-Chat-RL-v1)
[interactive version](https://whatmodel.sh/model/xdan-l1-chat-rl-v1) · [JSON](https://whatmodel.sh/api?format=json&all=1)