初始化项目，由ModelHub XC社区提供模型

Model: afrideva/evolvedSeeker_1_3-GGUF
Source: Original Platform

README.md

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+---
+base_model: TokenBender/evolvedSeeker_1_3
+inference: false
+model-index:
+- name: evolvedSeeker-1_3_v_0_0_1
+  results: []
+model_creator: TokenBender
+model_name: evolvedSeeker_1_3
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- generated_from_trainer
+- gguf
+- ggml
+- quantized
+- q2_k
+- q3_k_m
+- q4_k_m
+- q5_k_m
+- q6_k
+- q8_0
+---
+# TokenBender/evolvedSeeker_1_3-GGUF
+
+Quantized GGUF model files for [evolvedSeeker_1_3](https://huggingface.co/TokenBender/evolvedSeeker_1_3) from [TokenBender](https://huggingface.co/TokenBender)
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [evolvedseeker_1_3.fp16.gguf](https://huggingface.co/afrideva/evolvedSeeker_1_3-GGUF/resolve/main/evolvedseeker_1_3.fp16.gguf) | fp16 | 2.69 GB  |
+| [evolvedseeker_1_3.q2_k.gguf](https://huggingface.co/afrideva/evolvedSeeker_1_3-GGUF/resolve/main/evolvedseeker_1_3.q2_k.gguf) | q2_k | 631.71 MB  |
+| [evolvedseeker_1_3.q3_k_m.gguf](https://huggingface.co/afrideva/evolvedSeeker_1_3-GGUF/resolve/main/evolvedseeker_1_3.q3_k_m.gguf) | q3_k_m | 704.97 MB  |
+| [evolvedseeker_1_3.q4_k_m.gguf](https://huggingface.co/afrideva/evolvedSeeker_1_3-GGUF/resolve/main/evolvedseeker_1_3.q4_k_m.gguf) | q4_k_m | 873.58 MB  |
+| [evolvedseeker_1_3.q5_k_m.gguf](https://huggingface.co/afrideva/evolvedSeeker_1_3-GGUF/resolve/main/evolvedseeker_1_3.q5_k_m.gguf) | q5_k_m | 1.00 GB  |
+| [evolvedseeker_1_3.q6_k.gguf](https://huggingface.co/afrideva/evolvedSeeker_1_3-GGUF/resolve/main/evolvedseeker_1_3.q6_k.gguf) | q6_k | 1.17 GB  |
+| [evolvedseeker_1_3.q8_0.gguf](https://huggingface.co/afrideva/evolvedSeeker_1_3-GGUF/resolve/main/evolvedseeker_1_3.q8_0.gguf) | q8_0 | 1.43 GB  |
+
+
+
+## Original Model Card:
+# evolvedSeeker-1_3
+EvolvedSeeker v0.0.1 (First phase)
+
+This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on 50k instructions for 3 epochs.
+
+I have mostly curated instructions from evolInstruct datasets and some portions of glaive coder.
+
+Around 3k answers were modified via self-instruct.
+
+Collaborate or Consult me - [Twitter](https://twitter.com/4evaBehindSOTA), [Discord](https://discord.gg/ftEM63pzs2)
+
+*Recommended format is ChatML, Alpaca will work but take care of EOT token*
+
+#### Chat Model Inference
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("TokenBender/evolvedSeeker_1_3", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("TokenBender/evolvedSeeker_1_3", trust_remote_code=True).cuda()
+messages=[
+    { 'role': 'user', 'content': "write a program to reverse letters in each word in a sentence without reversing order of words in the sentence."}
+]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+# 32021 is the id of <|EOT|> token
+outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=32021)
+print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
+```
+
+## Model description
+
+First model of Project PIC (Partner-in-Crime) in 1.3B range.
+Almost all the work is pending right now for this model hence v0.0.1
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6398bf222da24ee95b51c8d8/Fl-pRCsC_lvnuoP734hsJ.png)
+
+## Intended uses & limitations
+
+Superfast Copilot
+Run near lossless quantized in 1G RAM.
+Useful for code dataset curation and evaluation.
+
+Limitations - This is a smol model, so smol brain, may have crammed a few things.
+Reasoning tests may fail beyond a certain point.
+
+## Training procedure
+SFT
+
+### Training results
+Humaneval Score - 68.29%
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6398bf222da24ee95b51c8d8/AFp6PxZ9ZP_xti4VWjen3.png)
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.0.1
+- Datasets 2.15.0
+- Tokenizers 0.15.0