Instructions to use jjee2/lora_recycle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jjee2/lora_recycle with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jjee2/lora_recycle",
	filename="Aratron1811__llama-3.1-8B-Instruct-abliterated-comrade/Meta-Llama-3.1-8B-Instruct-abliterated-TQ2_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use jjee2/lora_recycle with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jjee2/lora_recycle:TQ2_0
# Run inference directly in the terminal:
llama-cli -hf jjee2/lora_recycle:TQ2_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jjee2/lora_recycle:TQ2_0
# Run inference directly in the terminal:
llama-cli -hf jjee2/lora_recycle:TQ2_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf jjee2/lora_recycle:TQ2_0
# Run inference directly in the terminal:
./llama-cli -hf jjee2/lora_recycle:TQ2_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf jjee2/lora_recycle:TQ2_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jjee2/lora_recycle:TQ2_0

Use Docker

docker model run hf.co/jjee2/lora_recycle:TQ2_0

LM Studio
Jan
Ollama
How to use jjee2/lora_recycle with Ollama:
```
ollama run hf.co/jjee2/lora_recycle:TQ2_0
```

Unsloth Studio new

How to use jjee2/lora_recycle with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jjee2/lora_recycle to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jjee2/lora_recycle to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jjee2/lora_recycle to start chatting

Docker Model Runner
How to use jjee2/lora_recycle with Docker Model Runner:
```
docker model run hf.co/jjee2/lora_recycle:TQ2_0
```

Lemonade

How to use jjee2/lora_recycle with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jjee2/lora_recycle:TQ2_0

Run and chat with the model

lemonade run user.lora_recycle-TQ2_0

List all available models

lemonade list

jjee2 commited on Apr 14

Commit

0097741

verified ·

1 Parent(s): 98c1421

Add chchen__Llama-3.1-8B-Instruct-PsyCourse-doc-info-fold9

Browse files

Files changed (17) hide show

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chchen__Llama-3.1-8B-Instruct-PsyCourse-doc-info-fold9/.gitattributes +36 -0
chchen__Llama-3.1-8B-Instruct-PsyCourse-doc-info-fold9/README.md +75 -0
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chchen__Llama-3.1-8B-Instruct-PsyCourse-doc-info-fold9/README.md ADDED Viewed

	@@ -0,0 +1,75 @@

+---
+library_name: peft
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+- llama-factory
+- lora
+- generated_from_trainer
+model-index:
+- name: Llama-3.1-8B-Instruct-PsyCourse-doc-info-fold9
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# Llama-3.1-8B-Instruct-PsyCourse-doc-info-fold9
+This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the course-doc-info-train-fold9 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0588
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 16
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5.0
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.2819        | 0.3951 | 10   | 0.2725          |
+| 0.1426        | 0.7901 | 20   | 0.1502          |
+| 0.1008        | 1.1852 | 30   | 0.1117          |
+| 0.0861        | 1.5802 | 40   | 0.0898          |
+| 0.0701        | 1.9753 | 50   | 0.0760          |
+| 0.0667        | 2.3704 | 60   | 0.0695          |
+| 0.0528        | 2.7654 | 70   | 0.0660          |
+| 0.0625        | 3.1605 | 80   | 0.0622          |
+| 0.0423        | 3.5556 | 90   | 0.0613          |
+| 0.0437        | 3.9506 | 100  | 0.0601          |
+| 0.03          | 4.3457 | 110  | 0.0593          |
+| 0.0385        | 4.7407 | 120  | 0.0588          |
+### Framework versions
+- PEFT 0.12.0
+- Transformers 4.46.1
+- Pytorch 2.5.1+cu124
+- Datasets 3.1.0
+- Tokenizers 0.20.3

chchen__Llama-3.1-8B-Instruct-PsyCourse-doc-info-fold9/adapter_config.json ADDED Viewed

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "up_proj",
+    "o_proj",
+    "down_proj",
+    "gate_proj",
+    "v_proj",
+    "k_proj",
+    "q_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

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+{
+    "epoch": 4.938271604938271,
+    "eval_loss": 0.05879095941781998,
+    "eval_runtime": 26.1568,
+    "eval_samples_per_second": 1.72,
+    "eval_steps_per_second": 1.72,
+    "total_flos": 1.6673099211001037e+17,
+    "train_loss": 0.10069006884098053,
+    "train_runtime": 4781.8231,
+    "train_samples_per_second": 0.423,
+    "train_steps_per_second": 0.026
+}

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+{
+    "epoch": 4.938271604938271,
+    "eval_loss": 0.05879095941781998,
+    "eval_runtime": 26.1568,
+    "eval_samples_per_second": 1.72,
+    "eval_steps_per_second": 1.72
+}

chchen__Llama-3.1-8B-Instruct-PsyCourse-doc-info-fold9/llama3_lora_sft.yaml ADDED Viewed

	@@ -0,0 +1,46 @@

+### model
+model_name_or_path: meta-llama/Llama-3.1-8B-Instruct
+trust_remote_code: true
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+### dataset
+dataset: course-doc-info-train-fold9
+dataset_dir: data_private
+template: llama3
+cutoff_len: 8192
+# max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+### output
+output_dir: saves/psy-course-doc-info/Llama-3.1-8B-Instruct/train/fold9
+logging_steps: 1
+save_steps: 10
+plot_loss: true
+overwrite_output_dir: true
+save_total_limit: 3
+push_to_hub: true
+hub_model_id: chchen/Llama-3.1-8B-Instruct-PsyCourse-doc-info-fold9
+load_best_model_at_end: true
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 16
+learning_rate: 1.0e-4
+num_train_epochs: 5.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+enable_liger_kernel: true
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 10

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+  },
+  "additional_special_tokens": [
+    "<|eot_id|>",
+    "<|eom_id|>"
+  ],
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

chchen__Llama-3.1-8B-Instruct-PsyCourse-doc-info-fold9/train_results.json ADDED Viewed

	@@ -0,0 +1,8 @@

+{
+    "epoch": 4.938271604938271,
+    "total_flos": 1.6673099211001037e+17,
+    "train_loss": 0.10069006884098053,
+    "train_runtime": 4781.8231,
+    "train_samples_per_second": 0.423,
+    "train_steps_per_second": 0.026
+}

chchen__Llama-3.1-8B-Instruct-PsyCourse-doc-info-fold9/trainer_log.jsonl ADDED Viewed

	@@ -0,0 +1,138 @@

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