Instructions to use CodeIsAbstract/language_parser-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use CodeIsAbstract/language_parser-Q8_0-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="CodeIsAbstract/language_parser-Q8_0-GGUF", filename="language_parser-q8_0.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use CodeIsAbstract/language_parser-Q8_0-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_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 CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_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 CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use CodeIsAbstract/language_parser-Q8_0-GGUF with Ollama:
ollama run hf.co/CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0
- Unsloth Studio
How to use CodeIsAbstract/language_parser-Q8_0-GGUF 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 CodeIsAbstract/language_parser-Q8_0-GGUF 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 CodeIsAbstract/language_parser-Q8_0-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for CodeIsAbstract/language_parser-Q8_0-GGUF to start chatting
- Docker Model Runner
How to use CodeIsAbstract/language_parser-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0
- Lemonade
How to use CodeIsAbstract/language_parser-Q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0
Run and chat with the model
lemonade run user.language_parser-Q8_0-GGUF-Q8_0
List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0# Run inference directly in the terminal:
llama-cli -hf CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0Use 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 CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0# Run inference directly in the terminal:
./llama-cli -hf CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0Build 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 CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0# Run inference directly in the terminal:
./build/bin/llama-cli -hf CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0Use Docker
docker model run hf.co/CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0CodeIsAbstract/language_parser-Q8_0-GGUF
This model was converted to GGUF format from CodeIsAbstract/language_parser using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
USAGE:
user prompt:
<data>human natural language data</data>
<format type=xml/yaml/json>
field name: value_type
..
</format>
example:
<format type="yaml">
{submission_id: string, paper_title: string, review_round: number, submission_status: string, final_editor_decision_details: {decision_type: string, decision_date: date, editor_notes: string optional}, reviewers_feedback: [{reviewer_id: string, review_date: date, overall_score: number optional, contribution_summary: {novelty_assessment: string, rigor_assessment: string, clarity_assessment: string, ethical_concerns_raised: boolean}, specific_areas_for_improvement: [{section: string, issue_type: string, original_text_quote: string optional, suggested_change: string, severity: string}], confidential_comments: string optional}]}
</format>
<data>
**CONFIDENTIAL EDITORIAL REPORT**
**Subject:** Editorial Decision on Manuscript MS-2022-493-R2
**Title:** "Start Sit Put Prevent Room Return Law Pay Memory Than: Organized optimizing complexity"
This memo summarizes the outcome of review round 2. The final editorial decision of 'Major Revisions' was recorded on 2025-08-14, updating the manuscript's status to 'Major Revisions Required'. The handling editor's summary note states: "Opportunity hear can else course oil. Interest Democrat try. Figure evidence bad middle off call." This assessment reflects the consensus drawn from the 4 peer reviews received. Our internal analytics suggest this manuscript's topic is trending, which might explain the diverse reviewer opinions.
**Review Panel Feedback Synthesis:**
--------------------------------------
**Reviewer 1 (ID: RVR-T-996)** submitted their evaluation on 2024-11-17.
They provided an overall score of 4/10. Their assessment highlighted the work's novelty as 'fair' and its methodological rigor as 'poor'. Primary points for revision included:
- A 'suggestion' issue of type 'Grammar' was identified in the 'Discussion' section. The suggested action is to: "Best phone stuff accept place black describe white."
The comment seems to target text similar to '...Increase size public next put deal low a number similar....'.
- A 'suggestion' issue of type 'Unclear_Argument' was identified in the 'Methods' section. The suggested action is to: "Better partner treat decision cost around receive stock tree cup major born them character forget."
The comment seems to target text similar to '...Them eat middle hotel impact tree radio recognize....'.
- A 'minor' issue of type 'Methodological_Flaw' was identified in the 'Conclusion' section. The suggested action is to: "Scene source action to usually it majority radio chance page article where somebody."
*Confidential Note to Editor:* Be partner financial fill. Scene power head year gun TV decade.
**Reviewer 2 (ID: RVR-T-282)** submitted their evaluation on 2024-12-08.
They provided an overall score of 10/10. Their assessment highlighted the work's novelty as 'high' and its methodological rigor as 'good'. Primary points for revision included:
- A 'suggestion' issue of type 'Methodological_Flaw' was identified in the 'Abstract' section. The suggested action is to: "Though movement build will impact because nothing keep stop quality contain guess family teach conference."
The comment seems to target text similar to '...Difficult former continue eye yourself usually change maybe year learn throughout ahead....'.
- A 'critical' issue of type 'Formatting' was identified in the 'Overall' section. The suggested action is to: "Improve detail this no social method begin continue eye unit white eye position common discuss only read arm source hard oil feeling project bar opportunity series certain."
The comment seems to target text similar to '...Garden reveal ball surface growth power....'.
*Confidential Note to Editor:* Western though doctor. Speech soon explain whatever.
**Reviewer 3 (ID: RVR-D-397)** submitted their evaluation on 2025-06-18.
They provided an overall score of 6/10. Their assessment highlighted the work's novelty as 'poor' and its methodological rigor as 'medium'. Primary points for revision included:
- A 'minor' issue of type 'Literature_Gap' was identified in the 'Results' section. The suggested action is to: "Phone star happy capital series tax model analysis."
- A 'major' issue of type 'Formatting' was identified in the 'Methods' section. The suggested action is to: "Style half issue agency decision nor player risk man produce skin lead author particular nation old."
- A 'major' issue of type 'Methodological_Flaw' was identified in the 'Discussion' section. The suggested action is to: "Note not safe mention too ahead visit tax."
The comment seems to target text similar to '...Character common from ever daughter beyond how relationship country century generation space feeling free candidate mouth probably....'.
- A 'major' issue of type 'Scope' was identified in the 'Abstract' section. The suggested action is to: "Member who summer industry imagine network sure back tree movie play someone father season happen bar first ago defense."
**Reviewer 4 (ID: RVR-C-823)** submitted their evaluation on 2025-04-17.
Their assessment highlighted the work's novelty as 'good' and its methodological rigor as 'bad'. Primary points for revision included:
- A 'suggestion' issue of type 'Unclear_Argument' was identified in the 'Introduction' section. The suggested action is to: "Hour range increase line shoulder lead fast significant high human particularly."
The comment seems to target text similar to '...Performance receive radio beat dinner after next....'.
- A 'major' issue of type 'Unclear_Argument' was identified in the 'Abstract' section. The suggested action is to: "Allow notice season teach ground soldier indeed four majority day center ask show difficult may despite modern single apply phone."
The comment seems to target text similar to '...Recent drive traditional test every media line between finish reality bit fall teach require....'.
*Confidential Note to Editor:* Factor best share wife current. Consider movement first prepare technology.
**Conclusion:** The compiled feedback provides a clear path forward for the authors. This review cycle was completed slightly behind our quarterly schedule, an issue we're addressing with new workflow management software being rolled out next month. The decision letter is now ready for dispatch.
</data>
try response for yourself.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo CodeIsAbstract/language_parser-Q8_0-GGUF --hf-file language_parser-q8_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo CodeIsAbstract/language_parser-Q8_0-GGUF --hf-file language_parser-q8_0.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo CodeIsAbstract/language_parser-Q8_0-GGUF --hf-file language_parser-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo CodeIsAbstract/language_parser-Q8_0-GGUF --hf-file language_parser-q8_0.gguf -c 2048
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Model tree for CodeIsAbstract/language_parser-Q8_0-GGUF
Base model
CodeIsAbstract/language_parser
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0# Run inference directly in the terminal: llama-cli -hf CodeIsAbstract/language_parser-Q8_0-GGUF:Q8_0