93 lines
3.1 KiB
Markdown
93 lines
3.1 KiB
Markdown
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---
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base_model: mychen76/tinyllama-colorist-v2
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inference: false
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license: apache-2.0
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model_creator: mychen76
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model_name: tinyllama-colorist-v2
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quantized_by: afrideva
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tags:
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- gguf
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- ggml
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- quantized
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- q2_k
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- q3_k_m
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- q4_k_m
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- q5_k_m
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- q6_k
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- q8_0
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pipeline_tag: text-generation
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---
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# mychen76/tinyllama-colorist-v2-GGUF
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Quantized GGUF model files for [tinyllama-colorist-v2](https://huggingface.co/mychen76/tinyllama-colorist-v2) from [mychen76](https://huggingface.co/mychen76)
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [tinyllama-colorist-v2.q2_k.gguf](https://huggingface.co/afrideva/tinyllama-colorist-v2-GGUF/resolve/main/tinyllama-colorist-v2.q2_k.gguf) | q2_k | 482.15 MB |
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| [tinyllama-colorist-v2.q3_k_m.gguf](https://huggingface.co/afrideva/tinyllama-colorist-v2-GGUF/resolve/main/tinyllama-colorist-v2.q3_k_m.gguf) | q3_k_m | 549.85 MB |
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| [tinyllama-colorist-v2.q4_k_m.gguf](https://huggingface.co/afrideva/tinyllama-colorist-v2-GGUF/resolve/main/tinyllama-colorist-v2.q4_k_m.gguf) | q4_k_m | 667.82 MB |
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| [tinyllama-colorist-v2.q5_k_m.gguf](https://huggingface.co/afrideva/tinyllama-colorist-v2-GGUF/resolve/main/tinyllama-colorist-v2.q5_k_m.gguf) | q5_k_m | 782.05 MB |
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| [tinyllama-colorist-v2.q6_k.gguf](https://huggingface.co/afrideva/tinyllama-colorist-v2-GGUF/resolve/main/tinyllama-colorist-v2.q6_k.gguf) | q6_k | 903.42 MB |
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| [tinyllama-colorist-v2.q8_0.gguf](https://huggingface.co/afrideva/tinyllama-colorist-v2-GGUF/resolve/main/tinyllama-colorist-v2.q8_0.gguf) | q8_0 | 1.17 GB |
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## Original Model Card:
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MODEL: "mychen76/tinyllama-colorist-v2" - is a finetuned TinyLlama model using color dataset.
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MOTIVATION: A fun experimental model for using TinyLlama as Llama2 replacement for resource constraint environment.
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PROMPT FORMAT: "<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant:""
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MODEL USAGE:
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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from transformers import pipeline
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def print_color_space(hex_color):
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def hex_to_rgb(hex_color):
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hex_color = hex_color.lstrip('#')
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return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
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r, g, b = hex_to_rgb(hex_color)
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print(f'{hex_color}: \033[48;2;{r};{g};{b}m \033[0m')
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tokenizer = AutoTokenizer.from_pretrained(model_id_colorist_final)
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pipe = pipeline(
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"text-generation",
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model=model_id_colorist_final,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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from time import perf_counter
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start_time = perf_counter()
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prompt = formatted_prompt('give me a pure brown color')
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sequences = pipe(
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prompt,
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do_sample=True,
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temperature=0.1,
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top_p=0.9,
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num_return_sequences=1,
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eos_token_id=tokenizer.eos_token_id,
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max_new_tokens=12
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)
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for seq in sequences:
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print(f"Result: {seq['generated_text']}")
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output_time = perf_counter() - start_time
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print(f"Time taken for inference: {round(output_time,2)} seconds")
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```
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Result: #807070
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```
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Result: <|im_start|>user
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give me a pure brown color<|im_end|>
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<|im_start|>assistant: #807070<|im_end>
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Time taken for inference: 0.19 seconds
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```
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Dataset: "burkelibbey/colors"
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