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Model: bhaswata08/Llama-2-7b-chat-hf-function-calling-v3-AWQ
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---
license: llama2
---
# Model Card for bhaswata08/Llama-2-7b-chat-hf-function-calling-v3-AWQ
Model creator: Trelis
Original model: Llama-2-7b-chat-hf-function-calling-v3
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
- **Developed by:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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## Citation [optional]
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## Glossary [optional]
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{
"_name_or_path": "/root/.cache/huggingface/hub/models--Trelis--Llama-2-7b-chat-hf-function-calling-v3/snapshots/bf7dc2f5abd0df7e7c2796fc20b8fe422a428e93",
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],
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"hidden_act": "silu",
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"initializer_range": 0.02,
"intermediate_size": 11008,
"max_position_embeddings": 4096,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
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"quantization_config": {
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"group_size": 128,
"modules_to_not_convert": null,
"quant_method": "awq",
"version": "gemm",
"zero_point": true
},
"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.38.1",
"use_cache": false,
"vocab_size": 32000
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"chat_template": "{% for message in messages %}{% if message['role'] == 'function_metadata' %}[INST] You have access to the following functions. Use them if required:\n\n{{ message['content'] }}\n\n{% elif message['role'] == 'user' and loop.index0 == 1 %}{{ message['content'] }}{% elif message['role'] == 'assistant' %} [/INST]{{ message['content'] }}{{ eos_token }}{% elif message['role'] == 'function_call' %}[INST] [FUNCTION_CALL] {{ message['content'] }}{{ eos_token }}{% elif message['role'] == 'function_response' %} [/INST][FUNCTION_RESPONSE] Here is the response to the function call. If helpful, use it to respond to the user's question:{{ message['content'] }}{% elif message['role'] == 'user' and loop.index0 != 1 %}[INST] {{ message['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %} [/INST]{% endif %}",
"clean_up_tokenization_spaces": false,
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"legacy": false,
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<unk>",
"padding_side": "right",
"sp_model_kwargs": {},
"tokenizer_class": "LlamaTokenizer",
"unk_token": "<unk>",
"use_default_system_prompt": false
}