license: apache-2.0
base_model:
- kurakurai/Luth-1.7B-Instruct
language:
- fr
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
Luth-Instruct-GGUF
Luth-1.7B-Instruct is a French fine-tuned variant of the Qwen3-1.7B model, enhanced using the Luth-SFT dataset to significantly improve its capabilities in French instruction following, mathematics, and general knowledge while maintaining and even boosting its English performance. It was trained by full fine-tuning with Axolotl and later merged with the base Qwen3-1.7B, thus preserving its English competencies alongside marked improvements in French benchmarks. The model demonstrates strong performance on selected French and English benchmarks, including ifeval, gpqa-diamond, mmlu, math-500, arc-chall, and hellaswag, showing notable gains over comparable models in both languages. It is designed for tasks requiring bilingual proficiency with pronounced strength in French and is supported by available evaluation, training, and data scripts on GitHub. The model is suitable for instruction-following applications in contexts demanding enhanced French language understanding without compromising English language capabilities. It is openly accessible under an appropriate license for research and usage.
| Model Name | Model Size | Download Link |
|---|---|---|
| Luth-1.7B-Instruct-GGUF | 1.7B | Hugging Face |
| Luth-0.6B-Instruct-GGUF | 0.6B | Hugging Face |
Model Files
Luth-1.7B-Instruct
| File Name | Quant Type | File Size |
|---|---|---|
| Luth-1.7B-Instruct.BF16.gguf | BF16 | 3.45 GB |
| Luth-1.7B-Instruct.F16.gguf | F16 | 3.45 GB |
| Luth-1.7B-Instruct.F32.gguf | F32 | 6.89 GB |
| Luth-1.7B-Instruct.Q2_K.gguf | Q2_K | 778 MB |
| Luth-1.7B-Instruct.Q3_K_L.gguf | Q3_K_L | 1 GB |
| Luth-1.7B-Instruct.Q3_K_M.gguf | Q3_K_M | 940 MB |
| Luth-1.7B-Instruct.Q3_K_S.gguf | Q3_K_S | 867 MB |
| Luth-1.7B-Instruct.Q4_0.gguf | Q4_0 | 1.05 GB |
| Luth-1.7B-Instruct.Q4_1.gguf | Q4_1 | 1.14 GB |
| Luth-1.7B-Instruct.Q4_K.gguf | Q4_K | 1.11 GB |
| Luth-1.7B-Instruct.Q4_K_M.gguf | Q4_K_M | 1.11 GB |
| Luth-1.7B-Instruct.Q4_K_S.gguf | Q4_K_S | 1.06 GB |
| Luth-1.7B-Instruct.Q5_0.gguf | Q5_0 | 1.23 GB |
| Luth-1.7B-Instruct.Q5_1.gguf | Q5_1 | 1.32 GB |
| Luth-1.7B-Instruct.Q5_K.gguf | Q5_K | 1.26 GB |
| Luth-1.7B-Instruct.Q5_K_M.gguf | Q5_K_M | 1.26 GB |
| Luth-1.7B-Instruct.Q5_K_S.gguf | Q5_K_S | 1.23 GB |
| Luth-1.7B-Instruct.Q6_K.gguf | Q6_K | 1.42 GB |
| Luth-1.7B-Instruct.Q8_0.gguf | Q8_0 | 1.83 GB |
Luth-0.6B-Instruct
| File Name | Quant Type | File Size |
|---|---|---|
| Luth-0.6B-Instruct.BF16.gguf | BF16 | 1.2 GB |
| Luth-0.6B-Instruct.F16.gguf | F16 | 1.2 GB |
| Luth-0.6B-Instruct.F32.gguf | F32 | 2.39 GB |
| Luth-0.6B-Instruct.Q2_K.gguf | Q2_K | 296 MB |
| Luth-0.6B-Instruct.Q3_K_L.gguf | Q3_K_L | 368 MB |
| Luth-0.6B-Instruct.Q3_K_M.gguf | Q3_K_M | 347 MB |
| Luth-0.6B-Instruct.Q3_K_S.gguf | Q3_K_S | 323 MB |
| Luth-0.6B-Instruct.Q4_0.gguf | Q4_0 | 382 MB |
| Luth-0.6B-Instruct.Q4_1.gguf | Q4_1 | 409 MB |
| Luth-0.6B-Instruct.Q4_K.gguf | Q4_K | 397 MB |
| Luth-0.6B-Instruct.Q4_K_M.gguf | Q4_K_M | 397 MB |
| Luth-0.6B-Instruct.Q4_K_S.gguf | Q4_K_S | 383 MB |
| Luth-0.6B-Instruct.Q5_0.gguf | Q5_0 | 437 MB |
| Luth-0.6B-Instruct.Q5_1.gguf | Q5_1 | 464 MB |
| Luth-0.6B-Instruct.Q5_K.gguf | Q5_K | 444 MB |
| Luth-0.6B-Instruct.Q5_K_M.gguf | Q5_K_M | 444 MB |
| Luth-0.6B-Instruct.Q5_K_S.gguf | Q5_K_S | 437 MB |
| Luth-0.6B-Instruct.Q6_K.gguf | Q6_K | 495 MB |
| Luth-0.6B-Instruct.Q8_0.gguf | Q8_0 | 639 MB |
Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
