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@@ -14,7 +14,7 @@ language:
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  pipeline_tag: text-generation
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  ---
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- # DiTy/gemma-2-9b-it-function-calling
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  This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) for the **Function Calling** task on non-synthetic data,
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  fully annotated by humans only, on the English version of the <ins>*DiTy/function-calling*</ins> dataset.
@@ -24,11 +24,11 @@ In addition to **safetensors**, the model is available in **GGUF** formats (in t
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  | Filename | Quant type | File Size | Description |
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  | -------- | ---------- | --------- | ----------- |
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- | [gemma-2-9B-it-function-calling-F16.gguf](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling/blob/main/gemma-2-9B-it-function-calling-F16.gguf) | F16 | 18.5GB | Base model with float16 |
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- | [gemma-2-9B-it-function-calling-Q8_0.gguf](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling/blob/main/gemma-2-9B-it-function-calling-Q8_0.gguf) | Q8_0 | 9.83GB | Extremely high quality, generally unneeded but max available quant. |
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- | [gemma-2-9B-it-function-calling-Q6_K.gguf](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling/blob/main/gemma-2-9B-it-function-calling-Q6_K.gguf) | Q6_K | 7.59GB | Very high quality, near perfect, *recommended*. |
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- | [gemma-2-9B-it-function-calling-Q5_K_M.gguf](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling/blob/main/gemma-2-9B-it-function-calling-Q5_K_M.gguf) | Q5_K_M | 6.65GB | High quality, very usable. |
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- | [gemma-2-9B-it-function-calling-Q5_K_S.gguf](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling/blob/main/gemma-2-9B-it-function-calling-Q5_K_S.gguf) | Q5_K_S | 6.48GB | High quality, very usable. |
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  ## Model card tree
@@ -80,13 +80,13 @@ import torch
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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  model = AutoModelForCausalLM.from_pretrained(
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- "DiTy/gemma-2-9b-it-function-calling",
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  device_map="auto",
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  torch_dtype=torch.bfloat16, # use float16 or float32 if bfloat16 is not available to you.
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  cache_dir=PATH_TO_MODEL_DIR, # optional
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  )
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  tokenizer = AutoTokenizer.from_pretrained(
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- "DiTy/gemma-2-9b-it-function-calling",
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  cache_dir=PATH_TO_MODEL_DIR, # optional
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  )
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  ```
@@ -271,7 +271,7 @@ from transformers import pipeline
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  generation_pipeline = pipeline(
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  "text-generation",
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- model="DiTy/gemma-2-9b-it-function-calling",
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  model_kwargs={
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  "torch_dtype": torch.bfloat16, # use float16 or float32 if bfloat16 is not supported for you.
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  "cache_dir": PATH_TO_MODEL_DIR, # OPTIONAL
@@ -409,7 +409,7 @@ During the learning process, the validation error was approximated to the follow
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  | **Model** | **Generation Language** | **Approximately Validation Loss** |
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  | :-----: | :-----: | :-----: |
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- | **[DiTy/gemma-2-9b-it-function-calling](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling)** | EN | 0.5 |
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  | [DiTy/gemma-2-2b-it-function-calling](https://huggingface.co/DiTy/gemma-2-2b-it-function-calling) | EN | 0.66 |
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  ## Citation
 
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  pipeline_tag: text-generation
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  ---
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+ # DiTy/gemma-2-9b-it-function-calling-GGUF
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  This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) for the **Function Calling** task on non-synthetic data,
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  fully annotated by humans only, on the English version of the <ins>*DiTy/function-calling*</ins> dataset.
 
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  | Filename | Quant type | File Size | Description |
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  | -------- | ---------- | --------- | ----------- |
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+ | [gemma-2-9B-it-function-calling-F16.gguf](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling-GGUF/blob/main/gemma-2-9B-it-function-calling-F16.gguf) | F16 | 18.5GB | Base model with float16 |
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+ | [gemma-2-9B-it-function-calling-Q8_0.gguf](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling-GGUF/blob/main/gemma-2-9B-it-function-calling-Q8_0.gguf) | Q8_0 | 9.83GB | Extremely high quality, generally unneeded but max available quant. |
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+ | [gemma-2-9B-it-function-calling-Q6_K.gguf](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling-GGUF/blob/main/gemma-2-9B-it-function-calling-Q6_K.gguf) | Q6_K | 7.59GB | Very high quality, near perfect, *recommended*. |
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+ | [gemma-2-9B-it-function-calling-Q5_K_M.gguf](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling-GGUF/blob/main/gemma-2-9B-it-function-calling-Q5_K_M.gguf) | Q5_K_M | 6.65GB | High quality, very usable. |
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+ | [gemma-2-9B-it-function-calling-Q5_K_S.gguf](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling-GGUF/blob/main/gemma-2-9B-it-function-calling-Q5_K_S.gguf) | Q5_K_S | 6.48GB | High quality, very usable. |
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  ## Model card tree
 
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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  model = AutoModelForCausalLM.from_pretrained(
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+ "DiTy/gemma-2-9b-it-function-calling-GGUF",
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  device_map="auto",
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  torch_dtype=torch.bfloat16, # use float16 or float32 if bfloat16 is not available to you.
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  cache_dir=PATH_TO_MODEL_DIR, # optional
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  )
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  tokenizer = AutoTokenizer.from_pretrained(
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+ "DiTy/gemma-2-9b-it-function-calling-GGUF",
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  cache_dir=PATH_TO_MODEL_DIR, # optional
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  )
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  ```
 
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  generation_pipeline = pipeline(
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  "text-generation",
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+ model="DiTy/gemma-2-9b-it-function-calling-GGUF",
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  model_kwargs={
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  "torch_dtype": torch.bfloat16, # use float16 or float32 if bfloat16 is not supported for you.
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  "cache_dir": PATH_TO_MODEL_DIR, # OPTIONAL
 
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  | **Model** | **Generation Language** | **Approximately Validation Loss** |
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  | :-----: | :-----: | :-----: |
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+ | **[DiTy/gemma-2-9b-it-function-calling-GGUF](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling-GGUF)** | EN | 0.5 |
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  | [DiTy/gemma-2-2b-it-function-calling](https://huggingface.co/DiTy/gemma-2-2b-it-function-calling) | EN | 0.66 |
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  ## Citation