Instructions to use t4gandhi/Mistral-7B-v0.3-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use t4gandhi/Mistral-7B-v0.3-fine-tuned with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("t4gandhi/Mistral-7B-v0.3-fine-tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- adapter_config.json +4 -4
- adapter_model.safetensors +1 -1
adapter_config.json
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@@ -7,7 +7,7 @@
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"parent_library": "transformers.models.mistral.modeling_mistral",
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"unsloth_fixed": true
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},
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-
"base_model_name_or_path": "
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"bias": "none",
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"corda_config": null,
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"ensure_weight_tying": false,
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"revision": null,
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"target_modules": [
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"v_proj",
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"up_proj",
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"k_proj",
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"
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"down_proj",
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"q_proj",
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"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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"parent_library": "transformers.models.mistral.modeling_mistral",
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"unsloth_fixed": true
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},
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+
"base_model_name_or_path": "outputs/sft_merged",
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"bias": "none",
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"corda_config": null,
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"ensure_weight_tying": false,
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"revision": null,
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"target_modules": [
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"v_proj",
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"k_proj",
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"gate_proj",
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"down_proj",
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"q_proj",
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"o_proj",
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"up_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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adapter_model.safetensors
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 671149168
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:c5706f97b2630dec31302866b1f36e70f50f5c0a246b58e3ded9020134db2e87
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size 671149168
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